Single frame 4d detection using deep fusion of camera image, imaging radar and lidar point cloud

ABSTRACT

Embodiments of the present disclosure are directed to a method for object detection. The method includes receiving sensor data indicative of one or more objects for each of a camera subsystem, a LiDAR subsystem, and an imaging RADAR subsystem. The sensor data is received simultaneously and within one frame for each of the subsystems. The method also includes extracting one or more feature representations of the objects from camera image data, LiDAR point cloud data and imaging RADAR point cloud data and generating image feature maps, LiDAR feature maps and imaging RADAR feature maps. The method further includes combining the image feature maps, the LiDAR feature maps and the imaging RADAR feature maps to generate merged feature maps and generating object classification, object position, object dimensions, object heading and object velocity from the merged feature maps.

FIELD

The present disclosure is generally directed to vehicle methods andsystems and more particularly to vehicle methods and systems fordetecting objects using camera data, imaging radio detection and ranging(RADAR) data and light detection and ranging (LiDAR) data from a singleframe representing a scene from a route being navigated by the vehicle.

BACKGROUND

Vehicle navigation systems typically provide a display including agraphical representation of a map that indicates a current geographicalposition of a vehicle on the map as the map moves. Vehicles use sensorsto provide information about the surroundings of the vehicle such asdetecting objects. This information is used to augment or automatevehicle operations. Sensors include, but are not limited to, RADARsensors, camera sensors, LiDAR sensors, etc. RADAR systems provide aRADAR point cloud of radio-reflective points and LiDAR systems provide aLiDAR point cloud of reflective points, wherein the LiDAR point cloud ismuch denser than the RADAR point cloud and provides a better resolution.Object detection aims to classify objects, determine the position ofobjects, and determine the dimensions of objects. Traditionaltwo-dimensional (2D) object detection relies on camera images to obtainthe two dimensions of objects (i.e., length information and widthinformation) but with no depth information. Traditionalthree-dimensional (3D) object detection relies on LiDAR point cloudinformation and/or stereo camera information to obtain the position ofthe object (e.g., defined by the X,Y,Z coordinate points), thedimensions of the object (i.e., length, width, height), and theorientation of the object. 2D/3D object detection can be achieved usingcomputer vision and machine learning algorithms. Remarkable progress hasbeen achieved in 2D object detection with deep learning. However, 2Dobject detection algorithms have not been transferred well for 3D objectdetection due to the lower resolutions of 3D input data and the highcomplexity of estimating oriented bounding box around the objects.

Generally, a single frame of a LiDAR point cloud data and a single frameof stereo camera image data only provides 3D position and dimensioninformation for the objects. A tracking algorithm (e.g., Kalman filter,particle filter, etc.) is then required to derive the velocities of theobjects using multiple frames.

Conventional RADAR sensors (e.g., Bosch Gen4, Conti ARS430, etc.)provide very coarse resolutions and do not include elevation angleinformation. Conventional RADAR systems output a list of track objects,and these track objects are further associated and fused with the objectdetection results from LiDAR and camera systems. Next generation imagingRADAR systems provide much higher resolutions (e.g., less than 1° inazimuth and elevation angle resolution) and include elevation angleinformation as well. Moreover, each frame of imaging RADAR detectionincludes doppler information for the object from which the velocity ofthe object can be determined.

Accordingly, there is a need for improved methods and systems fordetecting objects, as well as estimating their velocities, in a singleframe for each simultaneously transmitted camera image data, LiDAR pointcloud data, and imaging RADAR point cloud data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a vehicle in accordance with embodiments of the presentdisclosure;

FIG. 2 shows a plan view of the vehicle in accordance with at least someembodiments of the present disclosure;

FIG. 3A is a block diagram of an embodiment of a communicationenvironment of the vehicle in accordance with embodiments of the presentdisclosure;

FIG. 3B is a block diagram of an embodiment of interior sensors withinthe vehicle in accordance with embodiments of the present disclosure;

FIG. 3C is a block diagram of an embodiment of a navigation system ofthe vehicle in accordance with embodiments of the present disclosure;

FIG. 4 shows an embodiment of the instrument panel of the vehicleaccording to one embodiment of the present disclosure;

FIG. 5 is a block diagram of an embodiment of a communications subsystemof the vehicle;

FIG. 6 is a block diagram of a computing environment associated with theembodiments presented herein;

FIG. 7 is a block diagram of a computing device associated with one ormore components described herein;

FIG. 8 is a block diagram of a four-dimensional (4D) object detectionsystem using camera, imaging RADAR and LiDAR fusion in accordance withembodiments of the present disclosure;

FIG. 9 is a block diagram of an example deep neural network (DNN)architecture for fusing LiDAR, imaging RADAR and camera inputs inaccordance with embodiments of the present disclosure;

FIG. 10 is a block diagram of another example DNN architecture forfusing LiDAR, imaging RADAR and camera inputs in accordance withembodiments of the present disclosure;

FIG. 11 is a flowchart illustrating an example process for 4D objectdetection by separate camera, imaging RADAR and LiDAR in accordance withembodiments of the present disclosure; and

FIG. 12 is a flowchart illustrating an example process for 4D objectdetection by camera, imaging RADAR and LiDAR fusion in accordance withembodiments of the present disclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure will be described in connectionwith a vehicle, and in some embodiments, an electric vehicle,rechargeable electric vehicle, and/or hybrid-electric vehicle andassociated systems.

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements.

FIG. 1 shows a perspective view of a vehicle 100 in accordance withembodiments of the present disclosure. The electric vehicle 100comprises a vehicle front 110, vehicle aft or rear 120, vehicle roof130, at least one vehicle side 160, a vehicle undercarriage 140, and avehicle interior 150. In any event, the vehicle 100 may include a frame104 and one or more body panels 108 mounted or affixed thereto. Thevehicle 100 may include one or more interior components (e.g.,components inside an interior space 150, or user space, of a vehicle100, etc.), exterior components (e.g., components outside of theinterior space 150, or user space, of a vehicle 100, etc.), drivesystems, controls systems, structural components, etc.

Although shown in the form of a car, it should be appreciated that thevehicle 100 described herein may include any conveyance or model of aconveyance, where the conveyance was designed for the purpose of movingone or more tangible objects, such as people, animals, cargo, and thelike. The term “vehicle” does not require that a conveyance moves or iscapable of movement. Typical vehicles may include but are in no waylimited to cars, trucks, motorcycles, busses, automobiles, trains,railed conveyances, boats, ships, marine conveyances, submarineconveyances, airplanes, space craft, flying machines, human-poweredconveyances, and the like.

In some embodiments, the vehicle 100 may include a number of sensors,devices, and/or systems that are capable of assisting in drivingoperations, e.g., autonomous or semi-autonomous control. Examples of thevarious sensors and systems may include, but are in no way limited to,one or more of cameras (e.g., independent, stereo, combined image,etc.), infrared (IR) sensors, radio frequency (RF) sensors, ultrasonicsensors (e.g., transducers, transceivers, etc.), radio detection andranging (RADAR) sensors (e.g., object-detection sensors and/or systems),light detection and ranging (LiDAR) sensors and/or systems, odometrysensors and/or devices (e.g., encoders, etc.), orientation sensors(e.g., accelerometers, gyroscopes, magnetometer, etc.), navigationsensors and systems (e.g., GPS, etc.), and other ranging, imaging,and/or object-detecting sensors. The sensors may be disposed in aninterior space 150 of the vehicle 100 and/or on an outside of thevehicle 100. In some embodiments, the sensors and systems may bedisposed in one or more portions of a vehicle 100 (e.g., the frame 104,a body panel, a compartment, etc.).

The vehicle sensors and systems may be selected and/or configured tosuit a level of operation associated with the vehicle 100. Among otherthings, the number of sensors used in a system may be altered toincrease or decrease information available to a vehicle control system(e.g., affecting control capabilities of the vehicle 100). Additionally,or alternatively, the sensors and systems may be part of one or moreadvanced driver assistance systems (ADAS) associated with a vehicle 100.In any event, the sensors and systems may be used to provide drivingassistance at any level of operation (e.g., from fully-manual tofully-autonomous operations, etc.) as described herein.

The various levels of vehicle control and/or operation can be describedas corresponding to a level of autonomy associated with a vehicle 100for vehicle driving operations. For instance, at Level 0, orfully-manual driving operations, a driver (e.g., a human driver) may beresponsible for all the driving control operations (e.g., steering,accelerating, braking, etc.) associated with the vehicle. Level 0 may bereferred to as a “No Automation” level. At Level 1, the vehicle may beresponsible for a limited number of the driving operations associatedwith the vehicle, while the driver is still responsible for most drivingcontrol operations. An example of a Level 1 vehicle may include avehicle in which the throttle control and/or braking operations may becontrolled by the vehicle (e.g., cruise control operations, etc.). Level1 may be referred to as a “Driver Assistance” level. At Level 2, thevehicle may collect information (e.g., via one or more drivingassistance systems, sensors, etc.) about an environment of the vehicle(e.g., surrounding area, roadway, traffic, ambient conditions, etc.) anduse the collected information to control driving operations (e.g.,steering, accelerating, braking, etc.) associated with the vehicle. In aLevel 2 autonomous vehicle, the driver may be required to perform otheraspects of driving operations not controlled by the vehicle. Level 2 maybe referred to as a “Partial Automation” level. It should be appreciatedthat Levels 0-2 all involve the driver monitoring the driving operationsof the vehicle.

At Level 3, the driver may be separated from controlling all the drivingoperations of the vehicle except when the vehicle makes a request forthe operator to act or intervene in controlling one or more drivingoperations. In other words, the driver may be separated from controllingthe vehicle unless the driver is required to take over for the vehicle.Level 3 may be referred to as a “Conditional Automation” level. At Level4, the driver may be separated from controlling all the drivingoperations of the vehicle and the vehicle may control driving operationseven when a user fails to respond to a request to intervene. Level 4 maybe referred to as a “High Automation” level. At Level 5, the vehicle cancontrol all the driving operations associated with the vehicle in alldriving modes. The vehicle in Level 5 may continually monitor traffic,vehicular, roadway, and/or environmental conditions while driving thevehicle. In Level 5, there is no human driver interaction required inany driving mode. Accordingly, Level 5 may be referred to as a “FullAutomation” level. It should be appreciated that in Levels 3-5 thevehicle, and/or one or more automated driving systems associated withthe vehicle, monitors the driving operations of the vehicle and thedriving environment.

As shown in FIG. 1, the vehicle 100 may, for example, include at leastone of a ranging and imaging system 112 (e.g., LiDAR, etc.), an imagingsensor 116A, 116F (e.g., camera, IR, etc.), a radio object-detection andranging system sensors 116B (e.g., RADAR, RF, etc.), ultrasonic sensors116C, and/or other object-detection sensors 116D, 116E. In someembodiments, the LiDAR system 112 and/or sensors may be mounted on aroof 130 of the vehicle 100. In one embodiment, the RADAR sensors 116Bmay be disposed at least at a front 110, aft 120, or side 160 of thevehicle 100. Among other things, the RADAR sensors may be used tomonitor and/or detect a position of other vehicles, pedestrians, and/orother objects near, or proximal to, the vehicle 100. While shownassociated with one or more areas of a vehicle 100, it should beappreciated that any of the sensors and systems 116A-K, 112 illustratedin FIGS. 1 and 2 may be disposed in, on, and/or about the vehicle 100 inany position, area, and/or zone of the vehicle 100.

Referring now to FIG. 2, a plan view of a vehicle 100 will be describedin accordance with embodiments of the present disclosure. In particular,FIG. 2 shows a vehicle sensing environment 200 at least partiallydefined by the sensors and systems 116A-K, 112 disposed in, on, and/orabout the vehicle 100. Each sensor 116A-K may include an operationaldetection range R and operational detection angle. The operationaldetection range R may define the effective detection limit, or distance,of the sensor 116A-K. In some cases, this effective detection limit maybe defined as a distance from a portion of the sensor 116A-K (e.g., alens, sensing surface, etc.) to a point in space offset from the sensor116A-K. The effective detection limit may define a distance, beyondwhich, the sensing capabilities of the sensor 116A-K deteriorate, failto work, or are unreliable. In some embodiments, the effective detectionlimit may define a distance, within which, the sensing capabilities ofthe sensor 116A-K are able to provide accurate and/or reliable detectioninformation. The operational detection angle may define at least oneangle of a span, or between horizontal and/or vertical limits, of asensor 116A-K. As can be appreciated, the operational detection limitand the operational detection angle of a sensor 116A-K together maydefine the effective detection zone 216A-D (e.g., the effectivedetection area, and/or volume, etc.) of a sensor 116A-K.

In some embodiments, the vehicle 100 may include a ranging and imagingsystem 112 such as LiDAR, or the like. The ranging and imaging system112 may be configured to detect visual information in an environmentsurrounding the vehicle 100. The visual information detected in theenvironment surrounding the ranging and imaging system 112 may beprocessed (e.g., via one or more sensor and/or system processors, etc.)to generate a complete 360-degree view of an environment 200 around thevehicle. The ranging and imaging system 112 may be configured togenerate changing 360-degree views of the environment 200 in real-time,for instance, as the vehicle 100 drives. In some cases, the ranging andimaging system 112 may have an effective detection limit 204 that issome distance from the center of the vehicle 100 outward over 360degrees. The effective detection limit 204 of the ranging and imagingsystem 112 defines a view zone 208 (e.g., an area and/or volume, etc.)surrounding the vehicle 100. Any object falling outside of the view zone208 is in the undetected zone 212 and would not be detected by theranging and imaging system 112 of the vehicle 100.

Sensor data and information may be collected by one or more sensors orsystems 116A-K, 112 of the vehicle 100 monitoring the vehicle sensingenvironment 200. This information may be processed (e.g., via aprocessor, computer-vision system, etc.) to determine targets (e.g.,objects, signs, people, markings, roadways, conditions, etc.) inside oneor more detection zones 208, 216A-D associated with the vehicle sensingenvironment 200. In some cases, information from multiple sensors 116A-Kmay be processed to form composite sensor detection information. Forexample, a first sensor 116A and a second sensor 116F may correspond toa first camera 116A and a second camera 116F aimed in a forwardtraveling direction of the vehicle 100. In this example, imagescollected by the cameras 116A, 116F may be combined to form stereo imageinformation. This composite information may increase the capabilities ofa single sensor in the one or more sensors 116A-K by, for example,adding the ability to determine depth associated with targets in the oneor more detection zones 208, 216A-D. Similar image data may be collectedby rear view cameras (e.g., sensors 116G, 116H) aimed in a rearwardtraveling direction vehicle 100.

In some embodiments, multiple sensors 116A-K may be effectively joinedto increase a sensing zone and provide increased sensing coverage. Forinstance, multiple RADAR sensors 116B disposed on the front 110 of thevehicle may be joined to provide a zone 216B of coverage that spansacross an entirety of the front 110 of the vehicle. In some cases, themultiple RADAR sensors 116B may cover a detection zone 216B thatincludes one or more other sensor detection zones 216A. Theseoverlapping detection zones may provide redundant sensing, enhancedsensing, and/or provide greater detail in sensing within a particularportion (e.g., zone 216A) of a larger zone (e.g., zone 216B).Additionally, or alternatively, the sensors 116A-K of the vehicle 100may be arranged to create a complete coverage, via one or more sensingzones 208, 216A-D around the vehicle 100. In some areas, the sensingzones 216C of two or more sensors 116D, 116E may intersect at an overlapzone 220. In some areas, the angle and/or detection limit of two or moresensing zones 216C, 216D (e.g., of two or more sensors 116E, 116J, 116K)may meet at a virtual intersection point 224.

The vehicle 100 may include a number of sensors 116E, 116G, 116H, 116J,116K disposed proximal to the rear 120 of the vehicle 100. These sensorscan include, but are in no way limited to, an imaging sensor, camera,IR, a radio object-detection and ranging sensors, RADAR, RF, ultrasonicsensors, and/or other object-detection sensors. Among other things,these sensors 116E, 116G, 116H, 116J, 116K may detect targets near orapproaching the rear of the vehicle 100. For example, another vehicleapproaching the rear 120 of the vehicle 100 may be detected by one ormore of the ranging and imaging system (e.g., LiDAR) 112, rear-viewcameras 116G, 116H, and/or rear facing RADAR sensors 116J, 116K. Asdescribed above, the images from the rear-view cameras 116G, 116H may beprocessed to generate a stereo view (e.g., providing depth associatedwith an object or environment, etc.) for targets visible to both cameras116G, 116H. As another example, the vehicle 100 may be driving and oneor more of the ranging and imaging system 112, front-facing cameras116A, 116F, front-facing RADAR sensors 116B, and/or ultrasonic sensors116C may detect targets in front of the vehicle 100. This approach mayprovide critical sensor information to a vehicle control system in atleast one of the autonomous driving levels described above. Forinstance, when the vehicle 100 is driving autonomously (e.g., Level 3,Level 4, or Level 5) and detects other vehicles stopped in a travelpath, the sensor detection information may be sent to the vehiclecontrol system of the vehicle 100 to control a driving operation (e.g.,braking, decelerating, etc.) associated with the vehicle 100 (in thisexample, slowing the vehicle 100 as to avoid colliding with the stoppedother vehicles). As yet another example, the vehicle 100 may beoperating and one or more of the ranging and imaging system 112, and/orthe side-facing sensors 116D, 116E (e.g., RADAR, ultrasonic, camera,combinations thereof, and/or other type of sensor), may detect targetsat a side of the vehicle 100. It should be appreciated that the sensors116A-K may detect a target that is both at a side 160 and a front 110 ofthe vehicle 100 (e.g., disposed at a diagonal angle to a centerline ofthe vehicle 100 running from the front 110 of the vehicle 100 to therear 120 of the vehicle). Additionally, or alternatively, the sensors116A-K may detect a target that is both, or simultaneously, at a side160 and a rear 120 of the vehicle 100 (e.g., disposed at a diagonalangle to the centerline of the vehicle 100).

FIGS. 3A-3C are block diagrams of an embodiment of a communicationenvironment 300 of the vehicle 100 in accordance with embodiments of thepresent disclosure. The communication system 300 may include one or morevehicle driving vehicle sensors and systems 304, sensor processors 340,sensor data memory 344, vehicle control system 348, communicationssubsystem 350, control data 364, computing devices 368, display devices372, and other components 374 that may be associated with a vehicle 100.These associated components may be electrically and/or communicativelycoupled to one another via at least one bus 360. In some embodiments,the one or more associated components may send and/or receive signalsacross a communication network 352 to at least one of a navigationsource 356A, a control source 356B, or some other entity 356N.

In accordance with at least some embodiments of the present disclosure,the communication network 352 may comprise any type of knowncommunication medium or collection of communication media and may useany type of protocols, such as SIP, TCP/IP, SNA, IPX, AppleTalk, and thelike, to transport messages between endpoints. The communication network352 may include wired and/or wireless communication technologies. TheInternet is an example of the communication network 352 that constitutesan Internet Protocol (IP) network consisting of many computers,computing networks, and other communication devices located all over theworld, which are connected through many telephone systems and othermeans. Other examples of the communication network 352 include, withoutlimitation, a standard Plain Old Telephone System (POTS), an IntegratedServices Digital Network (ISDN), the Public Switched Telephone Network(PSTN), a Local Area Network (LAN), such as an Ethernet network, aToken-Ring network and/or the like, a Wide Area Network (WAN), a virtualnetwork, including without limitation a virtual private network (“VPN”);the Internet, an intranet, an extranet, a cellular network, an infra-rednetwork; a wireless network (e.g., a network operating under any of theIEEE 802.9 suite of protocols, the Bluetooth® protocol known in the art,and/or any other wireless protocol), and any other type ofpacket-switched or circuit-switched network known in the art and/or anycombination of these and/or other networks. In addition, it can beappreciated that the communication network 352 need not be limited toany one network type, and instead may be comprised of a number ofdifferent networks and/or network types. The communication network 352may comprise a number of different communication media such as coaxialcable, copper cable/wire, fiber-optic cable, antennas fortransmitting/receiving wireless messages, and combinations thereof.

The driving vehicle sensors and systems 304 may include at least onenavigation 308 (e.g., global positioning system (GPS), etc.),orientation 312, odometry 316, LiDAR 320, RADAR 324, ultrasonic 328,camera 332, infrared (IR) 336, and/or other sensor or system 338. Thesedriving vehicle sensors and systems 304 may be similar, if notidentical, to the sensors and systems 116A-K, 112 described inconjunction with FIGS. 1 and 2.

The navigation sensor 308 may include one or more sensors havingreceivers and antennas that are configured to utilize a satellite-basednavigation system including a network of navigation satellites capableof providing geolocation and time information to at least one componentof the vehicle 100. Examples of the navigation sensor 308 as describedherein may include, but are not limited to, at least one of Garmin® GLO™family of GPS and GLONASS combination sensors, Garmin® GPS 15x™ familyof sensors, Garmin® GPS 16x™ family of sensors with high-sensitivityreceiver and antenna, Garmin® GPS 18x OEM family of high-sensitivity GPSsensors, Dewetron DEWE-VGPS series of GPS sensors, GlobalSat 1-Hz seriesof GPS sensors, other industry-equivalent navigation sensors and/orsystems, and may perform navigational and/or geolocation functions usingany known or future-developed standard and/or architecture.

The orientation sensor 312 may include one or more sensors configured todetermine an orientation of the vehicle 100 relative to at least onereference point. In some embodiments, the orientation sensor 312 mayinclude at least one pressure transducer, stress/strain gauge,accelerometer, gyroscope, and/or geomagnetic sensor. Examples of theorientation sensor 312 as described herein may include, but are notlimited to, at least one of Bosch Sensortec BMX 160 series low-powerabsolute orientation sensors, Bosch Sensortec BMX055 9-axis sensors,Bosch Sensortec BMI055 6-axis inertial sensors, Bosch Sensortec BMI1606-axis inertial sensors, Bosch Sensortec BMF055 9-axis inertial sensors(accelerometer, gyroscope, and magnetometer) with integrated Cortex M0+microcontroller, Bosch Sensortec BMP280 absolute barometric pressuresensors, Infineon TLV493D-A1B6 3D magnetic sensors, InfineonTLI493D-W1B6 3D magnetic sensors, Infineon TL family of 3D magneticsensors, Murata Electronics SCC2000 series combined gyro sensor andaccelerometer, Murata Electronics SCC1300 series combined gyro sensorand accelerometer, other industry-equivalent orientation sensors and/orsystems, which may perform orientation detection and/or determinationfunctions using any known or future-developed standard and/orarchitecture.

The odometry sensor and/or system 316 may include one or more componentsthat is configured to determine a change in position of the vehicle 100over time. In some embodiments, the odometry system 316 may utilize datafrom one or more other sensors and/or systems 304 in determining aposition (e.g., distance, location, etc.) of the vehicle 100 relative toa previously measured position for the vehicle 100. Additionally, oralternatively, the odometry sensors 316 may include one or moreencoders, Hall speed sensors, and/or other measurement sensors/devicesconfigured to measure a wheel speed, rotation, and/or number ofrevolutions made over time. Examples of the odometry sensor/system 316as described herein may include, but are not limited to, at least one ofInfineon TLE4924/26/27/28C high-performance speed sensors, InfineonTL4941plusC(B) single chip differential Hall wheel-speed sensors,Infineon TL5041plusC Giant Magnetoresistance (GMR) effect sensors,Infineon TL family of magnetic sensors, EPC Model 25SP Accu-CoderPro™incremental shaft encoders, EPC Model 30M compact incremental encoderswith advanced magnetic sensing and signal processing technology, EPCModel 925 absolute shaft encoders, EPC Model 958 absolute shaftencoders, EPC Model MA36S/MA63S/SA36S absolute shaft encoders, Dynapar™F18 commutating optical encoder, Dynapar™ HS35R family of phased arrayencoder sensors, other industry-equivalent odometry sensors and/orsystems, and may perform change in position detection and/ordetermination functions using any known or future-developed standardand/or architecture.

The LiDAR sensor/system 320 may include one or more componentsconfigured to measure distances to targets using laser illumination. Insome embodiments, the LiDAR sensor/system 320 may provide 3D imagingdata of an environment around the vehicle 100. The imaging data may beprocessed to generate a full 360-degree view of the environment aroundthe vehicle 100. The LiDAR sensor/system 320 may include a laser lightgenerator configured to generate a plurality of target illuminationlaser beams (e.g., laser light channels). In some embodiments, thisplurality of laser beams may be aimed at, or directed to, a rotatingreflective surface (e.g., a mirror) and guided outwardly from the LiDARsensor/system 320 into a measurement environment. The rotatingreflective surface may be configured to continually rotate 360 degreesabout an axis, such that the plurality of laser beams is directed in afull 360-degree range around the vehicle 100. A photodiode receiver ofthe LiDAR sensor/system 320 may detect when light from the plurality oflaser beams emitted into the measurement environment returns (e.g.,reflected echo) to the LiDAR sensor/system 320. The LiDAR sensor/system320 may calculate, based on a time associated with the emission of lightto the detected return of light, a distance from the vehicle 100 to theilluminated target. In some embodiments, the LiDAR sensor/system 320 maygenerate over 2.0 million points per second and have an effectiveoperational range of at least 100 meters. Examples of the LiDARsensor/system 320 as described herein may include, but are not limitedto, at least one of Velodyne® LiDAR™ HDL-64E 64-channel LiDAR sensors,Velodyne® LiDAR™ HDL-32E 32-channel LiDAR sensors, Velodyne® LiDAR™PUCK™ VLP-16 16-channel LiDAR sensors, Leica Geosystems Pegasus:Twomobile sensor platform, Garmin® LiDAR-Lite v3 measurement sensor,Quanergy M8 LiDAR sensors, Quanergy S3 solid state LiDAR sensor,LeddarTech® LeddarVU compact solid state fixed-beam LIDAR sensors, otherindustry-equivalent LiDAR sensors and/or systems, and may performilluminated target and/or obstacle detection in an environment aroundthe vehicle 100 using any known or future-developed standard and/orarchitecture.

The RADAR sensors 324 may include one or more radio components that areconfigured to detect objects/targets in an environment of the vehicle100. In some embodiments, the RADAR sensors 324 may determine adistance, position, and/or movement vector (e.g., angle, speed, etc.)associated with a target over time. The RADAR sensors 324 may include atransmitter configured to generate and emit electromagnetic waves (e.g.,radio, microwaves, etc.) and a receiver configured to detect returnedelectromagnetic waves. In some embodiments, the RADAR sensors 324 mayinclude at least one processor configured to interpret the returnedelectromagnetic waves and determine locational properties of targets.Examples of the RADAR sensors 324 as described herein may include, butare not limited to, at least one of Infineon RASIC™ RTN7735PLtransmitter and RRN7745PL/46PL receiver sensors, Autoliv ASP VehicleRADAR sensors, Delphi L2C0051TR 77 GHz ESR Electronically Scanning RADARsensors, Fujitsu Ten Ltd. Automotive Compact 77 GHz 3D Electronic ScanMillimeter Wave RADAR sensors, other industry-equivalent RADAR sensorsand/or systems, and may perform radio target and/or obstacle detectionin an environment around the vehicle 100 using any known orfuture-developed standard and/or architecture.

The ultrasonic sensors 328 may include one or more components that areconfigured to detect objects/targets in an environment of the vehicle100. In some embodiments, the ultrasonic sensors 328 may determine adistance, position, and/or movement vector (e.g., angle, speed, etc.)associated with a target over time. The ultrasonic sensors 328 mayinclude an ultrasonic transmitter and receiver, or transceiver,configured to generate and emit ultrasound waves and interpret returnedechoes of those waves. In some embodiments, the ultrasonic sensors 328may include at least one processor configured to interpret the returnedultrasonic waves and determine locational properties of targets.Examples of the ultrasonic sensors 328 as described herein may include,but are not limited to, at least one of Texas Instruments TIDA-00151automotive ultrasonic sensor interface IC sensors, MaxBotix® MB8450ultrasonic proximity sensor, MaxBotix® ParkSonar™-EZ ultrasonicproximity sensors, Murata Electronics MA40H1S-R open-structureultrasonic sensors, Murata Electronics MA40S4R/S open-structureultrasonic sensors, Murata Electronics MA58MF14-7N waterproof ultrasonicsensors, other industry-equivalent ultrasonic sensors and/or systems,and may perform ultrasonic target and/or obstacle detection in anenvironment around the vehicle 100 using any known or future-developedstandard and/or architecture.

The camera sensors 332 may include one or more components configured todetect image information associated with an environment of the vehicle100. In some embodiments, the camera sensors 332 may include a lens,filter, image sensor, and/or a digital image processor. It is an aspectof the present disclosure that multiple camera sensors 332 may be usedtogether to generate stereo images providing depth measurements.Examples of the camera sensors 332 as described herein may include, butare not limited to, at least one of ON Semiconductor® MT9V024 GlobalShutter VGA GS CMOS image sensors, Teledyne DALSA Falcon2 camerasensors, CMOSIS CMV50000 high-speed CMOS image sensors, otherindustry-equivalent camera sensors and/or systems, and may performvisual target and/or obstacle detection in an environment around thevehicle 100 using any known or future-developed standard and/orarchitecture.

The infrared (IR) sensors 336 may include one or more componentsconfigured to detect image information associated with an environment ofthe vehicle 100. The IR sensors 336 may be configured to detect targetsin low-light, dark, or poorly lit environments. The IR sensors 336 mayinclude an IR light emitting element (e.g., IR light emitting diode(LED), etc.), and an IR photodiode. In some embodiments, the IRphotodiode may be configured to detect returned IR light at or about thesame wavelength to that emitted by the IR light emitting element. Insome embodiments, the IR sensors 336 may include at least one processorconfigured to interpret the returned IR light and determine locationalproperties of targets. The IR sensors 336 may be configured to detectand/or measure a temperature associated with a target (e.g., an object,pedestrian, other vehicle, etc.). Examples of IR sensors 336 asdescribed herein may include, but are not limited to, at least one ofOpto Diode lead-salt IR array sensors, Opto Diode OD-850 Near-IR LEDsensors, Opto Diode SA/SHA727 steady state IR emitters and IR detectors,FLIR® LS microbolometer sensors, FLIR® TacFLIR 380-HD InSb MWIR FPA andHD MWIR thermal sensors, FLIR® VOx 640×480 pixel detector sensors,Delphi IR sensors, other industry-equivalent IR sensors and/or systems,and may perform IR visual target and/or obstacle detection in anenvironment around the vehicle 100 using any known or future-developedstandard and/or architecture.

The vehicle 100 can also include one or more interior sensors 337.Interior sensors 337 can measure characteristics of the insideenvironment of the vehicle 100. The interior sensors 337 may be asdescribed in conjunction with FIG. 3B.

A navigation system 302 can include any hardware and/or software used tonavigate the vehicle either manually or autonomously. The navigationsystem 302 may be as described in conjunction with FIG. 3C.

In some embodiments, the driving vehicle sensors and systems 304 mayinclude other sensors 338 and/or combinations of the sensors 306-337described above. Additionally, or alternatively, one or more of thesensors 306-337 described above may include one or more processorsconfigured to process and/or interpret signals detected by the one ormore sensors 306-337. In some embodiments, the processing of at leastsome sensor information provided by the vehicle sensors and systems 304may be processed by at least one sensor processor 340. Raw and/orprocessed sensor data may be stored in a sensor data memory 344 storagemedium. In some embodiments, the sensor data memory 344 may storeinstructions used by the sensor processor 340 for processing sensorinformation provided by the sensors and systems 304. In any event, thesensor data memory 344 may be a disk drive, optical storage device,solid-state storage device such as a random access memory (“RAM”) and/ora read-only memory (“ROM”), which can be programmable, flash-updateable,and/or the like.

The vehicle control system 348 may receive processed sensor informationfrom the sensor processor 340 and determine to control an aspect of thevehicle 100. Controlling an aspect of the vehicle 100 may includepresenting information via one or more display devices 372 associatedwith the vehicle, sending commands to one or more computing devices 368associated with the vehicle, and/or controlling a driving operation ofthe vehicle. In some embodiments, the vehicle control system 348 maycorrespond to one or more computing systems that control drivingoperations of the vehicle 100 in accordance with the Levels of drivingautonomy described above. In one embodiment, the vehicle control system348 may operate a speed of the vehicle 100 by controlling an outputsignal to the accelerator and/or braking system of the vehicle. In thisexample, the vehicle control system 348 may receive sensor datadescribing an environment surrounding the vehicle 100 and, based on thesensor data received, determine to adjust the acceleration, poweroutput, and/or braking of the vehicle 100. The vehicle control system348 may additionally control steering and/or other driving functions ofthe vehicle 100.

The vehicle control system 348 may communicate, in real-time, with thedriving sensors and systems 304 forming a feedback loop. In particular,upon receiving sensor information describing a condition of targets inthe environment surrounding the vehicle 100, the vehicle control system348 may autonomously make changes to a driving operation of the vehicle100. The vehicle control system 348 may then receive subsequent sensorinformation describing any change to the condition of the targetsdetected in the environment as a result of the changes made to thedriving operation. This continual cycle of observation (e.g., via thesensors, etc.) and action (e.g., selected control or non-control ofvehicle operations, etc.) allows the vehicle 100 to operate autonomouslyin the environment.

In some embodiments, the one or more components of the vehicle 100(e.g., the driving vehicle sensors 304, vehicle control system 348,display devices 372, etc.) may communicate across the communicationnetwork 352 to one or more entities 356A-N via a communicationssubsystem 350 of the vehicle 100. Embodiments of the communicationssubsystem 350 are described in greater detail in conjunction with FIG.5. For instance, the navigation sensors 308 may receive globalpositioning, location, and/or navigational information from a navigationsource 356A. In some embodiments, the navigation source 356A may be aglobal navigation satellite system (GNSS) similar, if not identical, toNAVSTAR GPS, GLONASS, EU Galileo, and/or the BeiDou Navigation SatelliteSystem (BDS) to name a few.

In some embodiments, the vehicle control system 348 may receive controlinformation from one or more control sources 356B. The control source356 may provide vehicle control information including autonomous drivingcontrol commands, vehicle operation override control commands, and thelike. The control source 356 may correspond to an autonomous vehiclecontrol system, a traffic control system, an administrative controlentity, and/or some other controlling server. It is an aspect of thepresent disclosure that the vehicle control system 348 and/or othercomponents of the vehicle 100 may exchange communications with thecontrol source 356 across the communication network 352 and via thecommunications subsystem 350.

Information associated with controlling driving operations of thevehicle 100 may be stored in a control data memory 364 storage medium.The control data memory 364 may store instructions used by the vehiclecontrol system 348 for controlling driving operations of the vehicle100, historical control information, autonomous driving control rules,and the like. In some embodiments, the control data memory 364 may be adisk drive, optical storage device, solid-state storage device such as arandom access memory (“RAM”) and/or a read-only memory (“ROM”), whichcan be programmable, flash-updateable, and/or the like.

In addition to the mechanical components described herein, the vehicle100 may include a number of user interface devices. The user interfacedevices receive and translate human input into a mechanical movement orelectrical signal or stimulus. The human input may be one or more ofmotion (e.g., body movement, body part movement, in two-dimensional orthree-dimensional space, etc.), voice, touch, and/or physicalinteraction with the components of the vehicle 100. In some embodiments,the human input may be configured to control one or more functions ofthe vehicle 100 and/or systems of the vehicle 100 described herein. Userinterfaces may include, but are in no way limited to, at least onegraphical user interface of a display device, steering wheel ormechanism, transmission lever or button (e.g., including park, neutral,reverse, and/or drive positions, etc.), throttle control pedal ormechanism, brake control pedal or mechanism, power control switch,communications equipment, etc.

FIG. 3B shows a block diagram of an embodiment of interior sensors 337for a vehicle 100. The interior sensors 337 may be arranged into one ormore groups, based at least partially on the function of the interiorsensors 337. For example, the interior space of a vehicle 100 mayinclude environmental sensors, user interface sensor(s), and/or safetysensors. Additionally or alternatively, there may be sensors associatedwith various devices inside the vehicle (e.g., smart phones, tablets,mobile computers, wearables, etc.)

Environmental sensors may comprise sensors configured to collect datarelating to the internal environment of a vehicle 100. Examples ofenvironmental sensors may include one or more of, but are not limitedto: oxygen/air sensors 301, temperature sensors 303, humidity sensors305, light/photo sensors 307, and more. The oxygen/air sensors 301 maybe configured to detect a quality or characteristic of the air in theinterior space 108 of the vehicle 100 (e.g., ratios and/or types ofgasses comprising the air inside the vehicle 100, dangerous gas levels,safe gas levels, etc.). Temperature sensors 303 may be configured todetect temperature readings of one or more objects, users 216, and/orareas of a vehicle 100. Humidity sensors 305 may detect an amount ofwater vapor present in the air inside the vehicle 100. The light/photosensors 307 can detect an amount of light present in the vehicle 100.Further, the light/photo sensors 307 may be configured to detect variouslevels of light intensity associated with light in the vehicle 100.

User interface sensors may comprise sensors configured to collect datarelating to one or more users (e.g., a driver and/or passenger(s)) in avehicle 100. As can be appreciated, the user interface sensors mayinclude sensors that are configured to collect data from users 216 inone or more areas of the vehicle 100. Examples of user interface sensorsmay include one or more of, but are not limited to: infrared sensors309, motion sensors 311, weight sensors 313, wireless network sensors315, biometric sensors 317, camera (or image) sensors 319, audio sensors321, and more.

Infrared sensors 309 may be used to measure IR light irradiating from atleast one surface, user, or other object in the vehicle 100. Among otherthings, the Infrared sensors 309 may be used to measure temperatures,form images (especially in low light conditions), identify users 216,and even detect motion in the vehicle 100.

The motion sensors 311 may detect motion and/or movement of objectsinside the vehicle 100. Optionally, the motion sensors 311 may be usedalone or in combination to detect movement. For example, a user may beoperating a vehicle 100 (e.g., while driving, etc.) when a passenger inthe rear of the vehicle 100 unbuckles a safety belt and proceeds to moveabout the vehicle 10. In this example, the movement of the passengercould be detected by the motion sensors 311. In response to detectingthe movement and/or the direction associated with the movement, thepassenger may be prevented from interfacing with and/or accessing atleast some of the vehicle control features. As can be appreciated, theuser may be alerted of the movement/motion such that the user can act toprevent the passenger from interfering with the vehicle controls.Optionally, the number of motion sensors in a vehicle may be increasedto increase an accuracy associated with motion detected in the vehicle100.

Weight sensors 313 may be employed to collect data relating to objectsand/or users in various areas of the vehicle 100. In some cases, theweight sensors 313 may be included in the seats and/or floor of avehicle 100. Optionally, the vehicle 100 may include a wireless networksensor 315. This sensor 315 may be configured to detect one or morewireless network(s) inside the vehicle 100. Examples of wirelessnetworks may include, but are not limited to, wireless communicationsutilizing Bluetooth®, Wi-Fi™, ZigBee, IEEE 802.11, and other wirelesstechnology standards. For example, a mobile hotspot may be detectedinside the vehicle 100 via the wireless network sensor 315. In thiscase, the vehicle 100 may determine to utilize and/or share the mobilehotspot detected via/with one or more other devices associated with thevehicle 100.

Biometric sensors 317 may be employed to identify and/or recordcharacteristics associated with a user. It is anticipated that biometricsensors 317 can include at least one of image sensors, IR sensors,fingerprint readers, weight sensors, load cells, force transducers,heart rate monitors, blood pressure monitors, and the like as providedherein.

The camera sensors 319 may record still images, video, and/orcombinations thereof. Camera sensors 319 may be used alone or incombination to identify objects, users, and/or other features, insidethe vehicle 100. Two or more camera sensors 319 may be used incombination to form, among other things, stereo and/or three-dimensional(3D) images. The stereo images can be recorded and/or used to determinedepth associated with objects and/or users in a vehicle 100. Further,the camera sensors 319 used in combination may determine the complexgeometry associated with identifying characteristics of a user. Forexample, the camera sensors 319 may be used to determine dimensionsbetween various features of a user's face (e.g., the depth/distance froma user's nose to a user's cheeks, a linear distance between the centerof a user's eyes, and more). These dimensions may be used to verify,record, and even modify characteristics that serve to identify a user.The camera sensors 319 may also be used to determine movement associatedwith objects and/or users within the vehicle 100. It should beappreciated that the number of image sensors used in a vehicle 100 maybe increased to provide greater dimensional accuracy and/or views of adetected image in the vehicle 100.

The audio sensors 321 may be configured to receive audio input from auser of the vehicle 100. The audio input from a user may correspond tovoice commands, conversations detected in the vehicle 100, phone callsmade in the vehicle 100, and/or other audible expressions made in thevehicle 100. Audio sensors 321 may include, but are not limited to,microphones and other types of acoustic-to-electric transducers orsensors. Optionally, the interior audio sensors 321 may be configured toreceive and convert sound waves into an equivalent analog or digitalsignal. The interior audio sensors 321 may serve to determine one ormore locations associated with various sounds in the vehicle 100. Thelocation of the sounds may be determined based on a comparison of volumelevels, intensity, and the like, between sounds detected by two or moreinterior audio sensors 321. For instance, a first audio sensors 321 maybe located in a first area of the vehicle 100 and a second audio sensors321 may be located in a second area of the vehicle 100. If a sound isdetected at a first volume level by the first audio sensors 321 A and asecond, higher, volume level by the second audio sensors 321 in thesecond area of the vehicle 100, the sound may be determined to be closerto the second area of the vehicle 100. As can be appreciated, the numberof sound receivers used in a vehicle 100 may be increased (e.g., morethan two, etc.) to increase measurement accuracy surrounding sounddetection and location, or source, of the sound (e.g., viatriangulation, etc.).

The safety sensors may comprise sensors configured to collect datarelating to the safety of a user and/or one or more components of avehicle 100. Examples of safety sensors may include one or more of, butare not limited to: force sensors 325, mechanical motion sensors 327,orientation sensors 329, restraint sensors 331, and more.

The force sensors 325 may include one or more sensors inside the vehicle100 configured to detect a force observed in the vehicle 100. Oneexample of a force sensor 325 may include a force transducer thatconverts measured forces (e.g., force, weight, pressure, etc.) intooutput signals. Mechanical motion sensors 327 may correspond toencoders, accelerometers, damped masses, and the like. Optionally, themechanical motion sensors 327 may be adapted to measure the force ofgravity (i.e., G-force) as observed inside the vehicle 100. Measuringthe G-force observed inside a vehicle 100 can provide valuableinformation related to a vehicle's acceleration, deceleration,collisions, and/or forces that may have been suffered by one or moreusers in the vehicle 100. Orientation sensors 329 can includeaccelerometers, gyroscopes, magnetic sensors, and the like that areconfigured to detect an orientation associated with the vehicle 100.

The restraint sensors 331 may correspond to sensors associated with oneor more restraint devices and/or systems in a vehicle 100. Seatbelts andairbags are examples of restraint devices and/or systems. As can beappreciated, the restraint devices and/or systems may be associated withone or more sensors that are configured to detect a state of thedevice/system. The state may include extension, engagement, retraction,disengagement, deployment, and/or other electrical or mechanicalconditions associated with the device/system.

The associated device sensors 323 can include any sensors that areassociated with a device in the vehicle 100. As previously stated,typical devices may include smart phones, tablets, laptops, mobilecomputers, and the like. It is anticipated that the various sensorsassociated with these devices can be employed by the vehicle controlsystem 348. For example, a typical smart phone can include, an imagesensor, an IR sensor, audio sensor, gyroscope, accelerometer, wirelessnetwork sensor, fingerprint reader, and more. It is an aspect of thepresent disclosure that one or more of these associated device sensors323 may be used by one or more subsystems of the vehicle 100.

FIG. 3C illustrates a GPS/Navigation subsystem(s) 302. The navigationsubsystem(s) 302 can be any present or future-built navigation systemthat may use location data, for example, from the Global PositioningSystem (GPS), to provide navigation information or control the vehicle100. The navigation subsystem(s) 302 can include several components,such as, one or more of, but not limited to: a GPS Antenna/receiver 331,a location module 333, a maps database 335, etc. Generally, the severalcomponents or modules 331-335 may be hardware, software, firmware,computer readable media, or combinations thereof.

A GPS Antenna/receiver 331 can be any antenna, GPS puck, and/or receivercapable of receiving signals from a GPS satellite or other navigationsystem. The signals may be demodulated, converted, interpreted, etc. bythe GPS Antenna/receiver 331 and provided to the location module 333.Thus, the GPS Antenna/receiver 331 may convert the time signals from theGPS system and provide a location (e.g., coordinates on a map) to thelocation module 333. Alternatively, the location module 333 caninterpret the time signals into coordinates or other locationinformation.

The location module 333 can be the controller of the satellitenavigation system designed for use in the vehicle 100. The locationmodule 333 can acquire position data, as from the GPS Antenna/receiver331, to locate the user or vehicle 100 on a road in the unit's mapdatabase 335. Using the road database 335, the location module 333 cangive directions to other locations along roads also in the database 335.When a GPS signal is not available, the location module 333 may applydead reckoning to estimate distance data from sensors 304 including oneor more of, but not limited to, a speed sensor attached to the drivetrain of the vehicle 100, a gyroscope, an accelerometer, etc.Additionally, or alternatively, the location module 333 may use knownlocations of Wi-Fi hotspots, cell tower data, etc. to determine theposition of the vehicle 100, such as by using time difference of arrival(TDOA) and/or frequency difference of arrival (FDOA) techniques.

The maps database 335 can include any hardware and/or software to storeinformation about maps, geographical information system (GIS)information, location information, etc. The maps database 335 caninclude any data definition or other structure to store the information.Generally, the maps database 335 can include a road database that mayinclude one or more vector maps of areas of interest. Street names,street numbers, house numbers, and other information can be encoded asgeographic coordinates so that the user can find some desireddestination by street address. Points of interest (waypoints) can alsobe stored with their geographic coordinates. For example, a point ofinterest may include speed cameras, fuel stations, public parking, and“parked here” (or “you parked here”) information. The maps database 335may also include road or street characteristics, for example, speedlimits, location of stop lights/stop signs, lane divisions, schoollocations, etc. The map database contents can be produced or updated bya server connected through a wireless system in communication with theInternet, even as the vehicle 100 is driven along existing streets,yielding an up-to-date map.

The vehicle control system 348, when operating in L4 or L5 and based onsensor information from the external and interior vehicle sensors, cancontrol the driving behavior of the vehicle in response to the currentvehicle location, sensed object information, sensed vehicle occupantinformation, vehicle-related information, exterior environmentalinformation, and navigation information from the maps database 335.

The sensed object information refers to sensed information regardingobjects external to the vehicle. Examples include animate objects suchas animals and attributes thereof (e.g., animal type, current spatiallocation, current activity, etc.), and pedestrians and attributesthereof (e.g., identity, age, sex, current spatial location, currentactivity, etc.), and the like and inanimate objects and attributesthereof such as other vehicles (e.g., current vehicle state or activity(parked or in motion or level of automation currently employed),occupant or operator identity, vehicle type (truck, car, etc.), vehiclespatial location, etc.), curbs (topography and spatial location),potholes (size and spatial location), lane division markers (type orcolor and spatial locations), signage (type or color and spatiallocations such as speed limit signs, yield signs, stop signs, and otherrestrictive or warning signs), traffic signals (e.g., red, yellow, blue,green, etc.), buildings (spatial locations), walls (height and spatiallocations), barricades (height and spatial location), and the like.

The sensed occupant information refers to sensed information regardingoccupants internal to the vehicle. Examples include the number andidentities of occupants and attributes thereof (e.g., seating position,age, sex, gaze direction, biometric information, authenticationinformation, preferences, historic behavior patterns (such as current orhistorical user driving behavior, historical user route, destination,and waypoint preferences), nationality, ethnicity and race, languagepreferences (e.g., Spanish, English, Chinese, etc.), current occupantrole (e.g., operator or passenger), occupant priority ranking (e.g.,vehicle owner is given a higher ranking than a child occupant),electronic calendar information (e.g., Outlook™), and medicalinformation and history, etc.

The vehicle-related information refers to sensed information regardingthe selected vehicle. Examples include vehicle manufacturer, type,model, year of manufacture, current geographic location, current vehiclestate or activity (parked or in motion or level of automation currentlyemployed), vehicle specifications and capabilities, currently sensedoperational parameters for the vehicle, and other information.

The exterior environmental information refers to sensed informationregarding the external environment of the selected vehicle. Examplesinclude road type (pavement, gravel, brick, etc.), road condition (e.g.,wet, dry, icy, snowy, etc.), weather condition (e.g., outsidetemperature, pressure, humidity, wind speed and direction, etc.),ambient light conditions (e.g., time-of-day), degree of development ofvehicle surroundings (e.g., urban or rural), and the like.

In a typical implementation, the automated vehicle control system 348,based on feedback from certain sensors, specifically the LiDAR and RADARsensors positioned around the circumference of the vehicle, constructs athree-dimensional map in spatial proximity to the vehicle that enablesthe automated vehicle control system 348 to identify and spatiallylocate animate and inanimate objects. Other sensors, such as inertialmeasurement units, gyroscopes, wheel encoders, sonar sensors, motionsensors to perform odometry calculations with respect to nearby movingexterior objects, and exterior facing cameras (e.g., to perform computervision processing) can provide further contextual information forgeneration of a more accurate three-dimensional map. The navigationinformation is combined with the three-dimensional map to provide short,intermediate and long-range course tracking and route selection. Thevehicle control system 348 processes real-world information as well asGPS data, and driving speed to determine accurately the precise positionof each vehicle, down to a few centimeters all while making correctionsfor nearby animate and inanimate objects.

The vehicle control system 348 can process in substantial real time theaggregate mapping information and models (or predicts) behavior ofoccupants of the current vehicle and other nearby animate or inanimateobjects and, based on the aggregate mapping information and modeledbehavior, issues appropriate commands regarding vehicle operation. Whilesome commands are hard-coded into the vehicle, such as stopping at redlights and stop signs, other responses are learned and recorded byprofile updates based on previous driving experiences. Examples oflearned behavior include a slow-moving or stopped vehicle or emergencyvehicle in a right lane suggests a higher probability that the carfollowing it will attempt to pass, a pot hole, rock, or other foreignobject in the roadway equates to a higher probability that a driver willswerve to avoid it, and traffic congestion in one lane means that otherdrivers moving in the same direction will have a higher probability ofpassing in an adjacent lane or by driving on the shoulder.

FIG. 4 shows one embodiment of the instrument panel 400 of the vehicle100. The instrument panel 400 of vehicle 100 comprises a steering wheel410, a vehicle operational display 420 (e.g., configured to presentand/or display driving data such as speed, measured air resistance,vehicle information, entertainment information, etc.), one or moreauxiliary displays 424 (e.g., configured to present and/or displayinformation segregated from the operational display 420, entertainmentapplications, movies, music, etc.), a heads-up display 434 (e.g.,configured to display any information previously described including,but in no way limited to, guidance information such as route todestination, or obstacle warning information to warn of a potentialcollision, or some or all primary vehicle operational data such asspeed, resistance, etc.), a power management display 428 (e.g.,configured to display data corresponding to electric power levels ofvehicle 100, reserve power, charging status, etc.), and an input device432 (e.g., a controller, touchscreen, or other interface deviceconfigured to interface with one or more displays in the instrumentpanel or components of the vehicle 100. The input device 432 may beconfigured as a joystick, mouse, touchpad, tablet, 3D gesture capturedevice, etc.). In some embodiments, the input device 432 may be used tomanually maneuver a portion of the vehicle 100 into a charging position(e.g., moving a charging plate to a desired separation distance, etc.).

While one or more of displays of instrument panel 400 may betouch-screen displays, it should be appreciated that the vehicleoperational display may be a display incapable of receiving touch input.For instance, the operational display 420 that spans across an interiorspace centerline 404 and across both a first zone 408A and a second zone408B may be isolated from receiving input from touch, especially from apassenger. In some cases, a display that provides vehicle operation orcritical systems information and interface may be restricted fromreceiving touch input and/or be configured as a non-touch display. Thistype of configuration can prevent dangerous mistakes in providing touchinput where such input may cause an accident or unwanted control.

In some embodiments, one or more displays of the instrument panel 400may be mobile devices and/or applications residing on a mobile devicesuch as a smart phone. Additionally, or alternatively, any of theinformation described herein may be presented to one or more portions420A-N of the operational display 420 or other display 424, 428, 434. Inone embodiment, one or more displays of the instrument panel 400 may bephysically separated or detached from the instrument panel 400. In somecases, a detachable display may remain tethered to the instrument panel.

The portions 420A-N of the operational display 420 may be dynamicallyreconfigured and/or resized to suit any display of information asdescribed. Additionally, or alternatively, the number of portions 420A-Nused to visually present information via the operational display 420 maybe dynamically increased or decreased as required and are not limited tothe configurations shown.

FIG. 5 illustrates a hardware diagram of communications componentry thatcan be optionally associated with the vehicle 100 in accordance withembodiments of the present disclosure.

The communications componentry can include one or more wired or wirelessdevices such as a transceiver(s) and/or modem that allows communicationsnot only between the various systems disclosed herein but also withother devices, such as devices on a network, and/or on a distributednetwork such as the Internet and/or in the cloud and/or with othervehicle(s).

The communications subsystem 350 can also include inter- andintra-vehicle communications capabilities such as hotspot and/or accesspoint connectivity for any one or more of the vehicle occupants and/orvehicle-to-vehicle communications.

Additionally, and while not specifically illustrated, the communicationssubsystem 350 can include one or more communications links (that can bewired or wireless) and/or communications busses (managed by the busmanager 574), including one or more of CANbus, OBD-II, ARCINC 429,Byteflight, CAN (Controller Area Network), D2B (Domestic Digital Bus),FlexRay, DC-BUS, IDB-1394, IEBus, I2C, ISO 9141-1/-2, J1708, J1587,J1850, J1939, ISO 11783, Keyword Protocol 2000, LIN (Local InterconnectNetwork), MOST (Media Oriented Systems Transport), Multifunction VehicleBus, SMARTwireX, SPI, VAN (Vehicle Area Network), and the like or ingeneral any communications protocol and/or standard(s).

The various protocols and communications can be communicated one or moreof wirelessly and/or over transmission media such as single wire,twisted pair, fiber optic, IEEE 1394, MIL-STD-1553, MIL-STD-1773,power-line communication, or the like. (All of the above standards andprotocols are incorporated herein by reference in their entirety.)

As discussed, the communications subsystem 350 enables communicationsbetween any of the inter-vehicle systems and subsystems as well ascommunications with non-collocated resources, such as those reachableover a network such as the Internet.

The communications subsystem 350, in addition to well-known componentry(which has been omitted for clarity), includes interconnected elementsincluding one or more of: one or more antennas 504, aninterleaver/deinterleaver 508, an analog front end (AFE) 512,memory/storage/cache 516, controller/microprocessor 520, MAC circuitry522, modulator/demodulator 524, encoder/decoder 528, a plurality ofconnectivity managers 534, 558, 562, 566, GPU 540, accelerator 544, amultiplexer/demultiplexer 552, transmitter 570, receiver 572 andadditional wireless radio components such as a Wi-Fi PHY/Bluetooth®module 580, a Wi-Fi/BT MAC module 584, additional transmitter(s) 588 andadditional receiver(s) 592. The various elements in the device 350 areconnected by one or more links/busses 5 (not shown, again for sake ofclarity).

The device 350 can have one more antennas 504, for use in wirelesscommunications such as multi-input multi-output (MIMO) communications,multi-user multi-input multi-output (MU-MIMO) communications Bluetooth®,LTE, 4G, 5G, Near-Field Communication (NFC), etc., and in general forany type of wireless communications. The antenna(s) 504 can include, butare not limited to one or more of directional antennas, omnidirectionalantennas, monopoles, patch antennas, loop antennas, microstrip antennas,dipoles, and any other antenna(s) suitable for communicationtransmission/reception. In an exemplary embodiment,transmission/reception using MIMO may require particular antennaspacing. In another exemplary embodiment, MIMO transmission/receptioncan enable spatial diversity allowing for different channelcharacteristics at each of the antennas. In yet another embodiment, MIMOtransmission/reception can be used to distribute resources to multipleusers for example within the vehicle 100 and/or in another vehicle.

Antenna(s) 504 generally interact with the Analog Front End (AFE) 512,which is needed to enable the correct processing of the receivedmodulated signal and signal conditioning for a transmitted signal. TheAFE 512 can be functionally located between the antenna and a digitalbaseband system in order to convert the analog signal into a digitalsignal for processing and vice-versa.

The subsystem 350 can also include a controller/microprocessor 520 and amemory/storage/cache 516. The subsystem 350 can interact with thememory/storage/cache 516 which may store information and operationsnecessary for configuring and transmitting or receiving the informationdescribed herein. The memory/storage/cache 516 may also be used inconnection with the execution of application programming or instructionsby the controller/microprocessor 520, and for temporary or long-termstorage of program instructions and/or data. As examples, thememory/storage/cache 520 may comprise a computer-readable device, RAM,ROM, DRAM, SDRAM, and/or other storage device(s) and media.

The controller/microprocessor 520 may comprise a general purposeprogrammable processor or controller for executing applicationprogramming or instructions related to the subsystem 350. Furthermore,the controller/microprocessor 520 can perform operations for configuringand transmitting/receiving information as described herein. Thecontroller/microprocessor 520 may include multiple processor cores,and/or implement multiple virtual processors. Optionally, thecontroller/microprocessor 520 may include multiple physical processors.By way of example, the controller/microprocessor 520 may comprise aspecially configured Application Specific Integrated Circuit (ASIC) orother integrated circuit, a digital signal processor(s), a controller, ahardwired electronic or logic circuit, a programmable logic device orgate array, a special purpose computer, or the like.

The subsystem 350 can further include a transmitter(s) 570, 588 andreceiver(s) 572, 592 which can transmit and receive signals,respectively, to and from other devices, subsystems and/or otherdestinations using the one or more antennas 504 and/or links/busses.Included in the subsystem 350 circuitry is the medium access control orMAC Circuitry 522. MAC circuitry 522 provides for controlling access tothe wireless medium. In an exemplary embodiment, the MAC circuitry 522may be arranged to contend for the wireless medium and configure framesor packets for communicating over the wired/wireless medium.

The subsystem 350 can also optionally contain a security module (notshown). This security module can contain information regarding but notlimited to, security parameters required to connect the device to one ormore other devices or other available network(s), and can include WEP orWPA/WPA-2 (optionally+AES and/or TKIP) security access keys, networkkeys, etc. The WEP security access key is a security password used byWi-Fi networks. Knowledge of this code can enable a wireless device toexchange information with an access point and/or another device. Theinformation exchange can occur through encoded messages with the WEPaccess code often being chosen by the network administrator. WPA is anadded security standard that is also used in conjunction with networkconnectivity with stronger encryption than WEP.

In some embodiments, the communications subsystem 350 also includes aGPU 540, an accelerator 544, a Wi-Fi/BT/BLE (Bluetooth® Low-Energy) PHYmodule 580 and a Wi-Fi/BT/BLE MAC module 584 and optional wirelesstransmitter 588 and optional wireless receiver 592. In some embodiments,the GPU 540 may be a graphics processing unit, or visual processingunit, comprising at least one circuit and/or chip that manipulates andchanges memory to accelerate the creation of images in a frame bufferfor output to at least one display device. The GPU 540 may include oneor more of a display device connection port, printed circuit board(PCB), a GPU chip, a metal-oxide-semiconductor field-effect transistor(MOSFET), memory (e.g., single data rate random-access memory (SDRAM),double data rate random-access memory (DDR) RAM, etc., and/orcombinations thereof), a secondary processing chip (e.g., handling videoout capabilities, processing, and/or other functions in addition to theGPU chip, etc.), a capacitor, heatsink, temperature control or coolingfan, motherboard connection, shielding, and the like.

The various connectivity managers 534, 558, 562, 566 manage and/orcoordinate communications between the subsystem 350 and one or more ofthe systems disclosed herein and one or more other devices/systems. Theconnectivity managers 534, 558, 562, 566 include a charging connectivitymanager 534, a vehicle database connectivity manager 558, a remoteoperating system connectivity manager 562, and a sensor connectivitymanager 566.

The charging connectivity manager 534 can coordinate not only thephysical connectivity between the vehicle 100 and a chargingdevice/vehicle, but can also communicate with one or more of a powermanagement controller, one or more third parties, and optionally abilling system(s). As an example, the vehicle 100 can establishcommunications with the charging device/vehicle to one or more ofcoordinate interconnectivity between the two (e.g., by spatiallyaligning the charging receptacle on the vehicle with the charger on thecharging vehicle) and optionally share navigation information. Oncecharging is complete, the amount of charge provided can be tracked andoptionally forwarded to, for example, a third party for billing. Inaddition to being able to manage connectivity for the exchange of power,the charging connectivity manager 534 can also communicate information,such as billing information to the charging vehicle and/or a thirdparty. This billing information could be, for example, the owner of thevehicle, the driver/occupant(s) of the vehicle, company information, orin general any information usable to charge the appropriate entity forthe power received.

The vehicle database connectivity manager 558 allows the subsystem toreceive and/or share information stored in the vehicle database. Thisinformation can be shared with other vehicle components/subsystemsand/or other entities, such as third parties and/or charging systems.The information can also be shared with one or more vehicle occupantdevices, such as an app (application) on a mobile device the driver usesto track information about the vehicle 100 and/or a dealer orservice/maintenance provider. In general, any information stored in thevehicle database can optionally be shared with any one or more otherdevices optionally subject to any privacy or confidentiallyrestrictions.

The remote operating system connectivity manager 562 facilitatescommunications between the vehicle 100 and any one or more autonomousvehicle systems. These communications can include one or more ofnavigation information, vehicle information, other vehicle information,weather information, occupant information, or in general any informationrelated to the remote operation of the vehicle 100.

The sensor connectivity manager 566 facilitates communications betweenany one or more of the vehicle sensors (e.g., the driving vehiclesensors and systems 304, etc.) and any one or more of the other vehiclesystems. The sensor connectivity manager 566 can also facilitatecommunications between any one or more of the sensors and/or vehiclesystems and any other destination, such as a service company, app, or ingeneral to any destination where sensor data is needed.

In accordance with one exemplary embodiment, any of the communicationsdiscussed herein can be communicated via the conductor(s) used forcharging. One exemplary protocol usable for these communications isPower-line communication (PLC). PLC is a communication protocol thatuses electrical wiring to simultaneously carry both data, andAlternating Current (AC) electric power transmission or electric powerdistribution. It is also known as power-line carrier, power-line digitalsubscriber line (PDSL), mains communication, power-linetelecommunications, or power-line networking (PLN). For DC environmentsin vehicles PLC can be used in conjunction with CAN-bus, LIN-bus overpower line (DC-LIN) and DC-BUS.

The communications subsystem can also optionally manage one or moreidentifiers, such as an IP (Internet Protocol) address(es), associatedwith the vehicle and one or other system or subsystems or componentsand/or devices therein. These identifiers can be used in conjunctionwith any one or more of the connectivity managers as discussed herein.

FIG. 6 illustrates a block diagram of a computing environment 600 thatmay function as the servers, user computers, or other systems providedand described herein. The computing environment 600 includes one or moreuser computers, or computing devices, such as a vehicle computing device604, a communication device 608, and/or more 612. The computing devices604, 608, 612 may include general purpose personal computers (including,merely by way of example, personal computers, and/or laptop computersrunning various versions of Microsoft Corp.'s Windows® and/or AppleCorp.'s Macintosh® operating systems) and/or workstation computersrunning any of a variety of commercially-available UNIX® or UNIX-likeoperating systems. These computing devices 604, 608, 612 may also haveany of a variety of applications, including for example, database clientand/or server applications, and web browser applications. Alternatively,the computing devices 604, 608, 612 may be any other electronic device,such as a thin-client computer, Internet-enabled mobile telephone,and/or personal digital assistant, capable of communicating via anetwork 352 and/or displaying and navigating web pages or other types ofelectronic documents or information. Although the exemplary computingenvironment 600 is shown with two computing devices, any number of usercomputers or computing devices may be supported.

The computing environment 600 may also include one or more servers 614,616. In this example, server 614 is shown as a web server and server 616is shown as an application server. The web server 614, which may be usedto process requests for web pages or other electronic documents fromcomputing devices 604, 608, 612. The web server 614 can be running anoperating system including any of those discussed above, as well as anycommercially-available server operating systems. The web server 614 canalso run a variety of server applications, including SIP (SessionInitiation Protocol) servers, HTTP(s) servers, FTP servers, CGI servers,database servers, Java® servers, and the like. In some instances, theweb server 614 may publish operations available operations as one ormore web services.

The computing environment 600 may also include one or more file andor/application servers 616, which can, in addition to an operatingsystem, include one or more applications accessible by a client runningon one or more of the computing devices 604, 608, 612. The server(s) 616and/or 614 may be one or more general purpose computers capable ofexecuting programs or scripts in response to the computing devices 604,608, 612. As one example, the server 616, 614 may execute one or moreweb applications. The web application may be implemented as one or morescripts or programs written in any programming language, such as Java®,C, C#®, or C++, and/or any scripting language, such as Perl, Python, orTCL, as well as combinations of any programming/scripting languages. Theapplication server(s) 616 may also include database servers, includingwithout limitation those commercially available from Oracle®,Microsoft®, Sybase®, IBM® and the like, which can process requests fromdatabase clients running on a computing device 604, 608, 612.

The web pages created by the server 614 and/or 616 may be forwarded to acomputing device 604, 608, 612 via a web (file) server 614, 616.Similarly, the web server 614 may be able to receive web page requests,web services invocations, and/or input data from a computing device 604,608, 612 (e.g., a user computer, etc.) and can forward the web pagerequests and/or input data to the web (application) server 616. Infurther embodiments, the server 616 may function as a file server.Although for ease of description, FIG. 6 illustrates a separate webserver 614 and file/application server 616, those skilled in the artwill recognize that the functions described with respect to servers 614,616 may be performed by a single server and/or a plurality ofspecialized servers, depending on implementation-specific needs andparameters. The computer systems 604, 608, 612, web (file) server 614and/or web (application) server 616 may function as the system, devices,or components described in FIGS. 1-6.

The computing environment 600 may also include a database 618. Thedatabase 618 may reside in a variety of locations. By way of example,database 618 may reside on a storage medium local to (and/or residentin) one or more of the computers 604, 608, 612, 614, 616. Alternatively,it may be remote from any or all of the computers 604, 608, 612, 614,616, and in communication (e.g., via the network 352) with one or moreof these. The database 618 may reside in a storage-area network (“SAN”)familiar to those skilled in the art. Similarly, any necessary files forperforming the functions attributed to the computers 604, 608, 612, 614,616 may be stored locally on the respective computer and/or remotely, asappropriate. The database 618 may be a relational database, such asOracle 20i®, that is adapted to store, update, and retrieve data inresponse to SQL-formatted commands.

FIG. 7 illustrates one embodiment of a computer system 700 upon whichthe servers, user computers, computing devices, or other systems orcomponents described above may be deployed or executed. The computersystem 700 is shown comprising hardware elements that may beelectrically coupled via a bus 704. The hardware elements may includeone or more central processing units (CPUs) 708; one or more inputdevices 712 (e.g., a mouse, a keyboard, etc.); and one or more outputdevices 716 (e.g., a display device, a printer, etc.). The computersystem 700 may also include one or more storage devices 720. By way ofexample, storage device(s) 720 may be disk drives, optical storagedevices, solid-state storage devices such as a random-access memory(“RAM”) and/or a read-only memory (“ROM”), which can be programmable,flash-updateable and/or the like.

The computer system 700 may additionally include a computer-readablestorage media reader 724; a communications system 728 (e.g., a modem, anetwork card (wireless or wired), an infra-red communication device,etc.); and working memory 736, which may include RAM and ROM devices asdescribed above. The computer system 700 may also include a processingacceleration unit 732, which can include a DSP, a special-purposeprocessor, and/or the like.

The computer-readable storage media reader 724 can further be connectedto a computer-readable storage medium, together (and, optionally, incombination with storage device(s) 720) comprehensively representingremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containingcomputer-readable information. The communications system 728 may permitdata to be exchanged with a network and/or any other computer describedabove with respect to the computer environments described herein.Moreover, as disclosed herein, the term “storage medium” may representone or more devices for storing data, including read only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information.

The computer system 700 may also comprise software elements, shown asbeing currently located within a working memory 736, including anoperating system 740 and/or other code 744. It should be appreciatedthat alternate embodiments of a computer system 700 may have numerousvariations from that described above. For example, customized hardwaremight also be used and/or particular elements might be implemented inhardware, software (including portable software, such as applets), orboth. Further, connection to other computing devices such as networkinput/output devices may be employed.

Examples of the processors 340, 708 as described herein may include, butare not limited to, at least one of Qualcomm® Snapdragon® 800 and 801,Qualcomm® Snapdragon® 620 and 615 with 4G LTE Integration and 64-bitcomputing, Apple® A7 processor with 64-bit architecture, Apple® M7motion coprocessors, Samsung® Exynos® series, the Intel® Core™ family ofprocessors, the Intel® Xeon® family of processors, the Intel® Atom™family of processors, the Intel Itanium® family of processors, Intel®Core® i5-4670K and i7-4770K 22 nm Haswell, Intel® Core® i5-3570K 22 nmIvy Bridge, the AMD® FX™ family of processors, AMD® FX-4300, FX-6300,and FX-8350 32 nm Vishera, AMD® Kaveri processors, Texas Instruments®Jacinto C6000™ automotive infotainment processors, Texas Instruments®OMAP™ automotive-grade mobile processors, ARM® Cortex™-M processors,ARM® Cortex-A and ARIVI926EJ-S™ processors, other industry-equivalentprocessors, and may perform computational functions using any known orfuture-developed standard, instruction set, libraries, and/orarchitecture.

Embodiments of the present disclosure are directed to object detectionand the detection of the velocities of the objects. More specifically,embodiments of the present disclosure are directed to four-dimensional(4D) object detection (i.e., X,Y,Z, v) where the (x,y,z) informationrepresents the position of an object and the (v) information representsthe velocity of the object.

FIG. 8 is a block diagram of a four-dimensional (4D) object detectionsystem 800 using camera data, imaging RADAR data and LiDAR data fusionin accordance with embodiments of the present disclosure. The system 800includes a deep neural network (DNN) 816 which receives inputs 804, 808,812 and provides an output 820. In general, neural networks are a set ofalgorithms, modeled loosely after the human brain, that are designed torecognize patterns. They interpret sensory data through a kind ofmachine perception, labeling or clustering raw input. The patterns theyrecognize are numerical, contained in vectors, into which all real-worlddata, be it images, sound, text or time series, must be translated.Neural networks help to cluster and classify data which assists ingrouping unlabeled or unstructured data according to similarities amongthe example inputs and classify data when the data have a labeleddataset on which to train.

Neural networks can also extract features that are fed to otheralgorithms for clustering and classification. DNNs are components oflarger machine-learning applications involving algorithms forreinforcement learning, classification, and regression. The DNNs have aninput layer, an output layer and at least one hidden layer in between.Each layer performs specific types of sorting and ordering in a processthat is referred to as “feature hierarchy.” Deep learning is alsoassociated the with DNNs, as deep learning represents a specific form ofmachine learning where technologies using aspects of artificialintelligence seek to classify and order information in ways that gobeyond simple input/output protocols. Although DNN 816 is used in thisexample embodiment of the present disclosure, other complex andsophisticated algorithms can be used without departing from the spiritand scope of the present disclosure. For example, other types ofconventional detection algorithms or equivalent algorithms can be used.DNN 816 is stored for example, in one or more of theprocessors/computers such as sensor processor(s) 340, computingdevice(s) 368, vehicle computing device 604, communication devices 608and 612, CPU 708 and/or servers 614 and 616.

A single frame of each of camera image 804 data, imaging RADAR pointcloud 808 data and LiDAR point cloud 812 data are simultaneouslyreceived as inputs to DNN 816. A camera image is a set of pixels andeach pixel contains information of R, G, B values. The LiDAR point cloudis a combination of points and each point contains X,Y,Z locationinformation and reflection intensity of the point. A RADAR point cloudis a combination of detected points from the targets and theenvironment. Each point contains location information X,Y,Z, location ofa point, reflection intensity of the point as well as a Doppler velocitybetween the point and the RADAR device. The single frame of camera image804 data is provided by camera sensor 332 and the single frame ofimaging RADAR point cloud 808 data is provided by the RADAR sensor 324.The single frame of LiDAR point cloud 812 data is provided by LiDARsensor 320. Each of the single frames of camera image 804 data, imagingRADAR point cloud 808 data and LiDAR point cloud 812 data issynchronized such that each frame is taken at the same time and in thesame location representing a scene from a route being navigated by thevehicle 100. After processing by the one or more processors/computersidentified above utilizing DNN 816, output 820 includes the followingparameters for detected objects: object classification (e.g., car,pedestrian, etc.); 3D position of the object (e.g., X,Y,Z coordinates ofthe object); 3D dimensions of the object (e.g., length, width, height ofthe object); the direction of the object (e.g., heading); and thevelocity of the object.

As for the imaging RADAR point cloud 808, a detected point of an objecthas location (x,y) and velocity (v) at a certain heading. Here the term“heading” is defined as the angle between the direction the is pointingand the horizontal x-axis on a bird's eye view plane. The Dopplervelocity of this point is calculated by Equation 1 as follows:

$\begin{matrix}{{{Doppler}\mspace{14mu}{velocity}} = {\frac{{v*{\cos({heading})}*x} + {v*{\sin({heading})}*y}}{\sqrt{x^{2} + y^{2}}}.}} & (1)\end{matrix}$

If an imaging RADAR detects multiple reflecting points from an objectsurface, the Doppler information carries the target velocityinformation. Moreover, the Doppler information is subject to somemeasurement noise.

The camera image 804 data requires more computing power to analyze allof the camera data, but can detect colors and interpret text such asreading road signs. In general, camera sensors can perceive color andtextual information from an environment and are good at classifyingobjects, but their detection range is limited, and they perform poorlyin limited lighting or adverse weather conditions. The imaging RADARpoint cloud 808 data can still provide information in the snow, rain, orfog since RADAR sensors are resistant to bad weather conditions wherecameras and LiDARs will not work. In general, RADAR sensor providesprecise distance and velocity information and work well in inclementweather conditions but have a rather low resolution. Although LiDARpoint cloud 812 data can identify some shapes, camera sensors have adistinct advantage. In general, LiDAR sensor provide precise distanceinformation, have ranges that can exceed 100 meter and are able todetect small objects. LiDAR sensors also work well at night but do notprovide color information and their performance decreases in heavy rain.

FIG. 9 is a block diagram of an example DNN architecture 900 for fusingLiDAR, imaging RADAR and camera inputs in accordance with embodiments ofthe present disclosure. The architecture 900 includes multi-modalcalibration and time synchronization block 952, camera image 904 data,LiDAR point cloud 908 data, imaging RADAR point cloud 912 data, DNNfeature extractors 916, 924 and 928, image feature maps 932, LiDARfeature maps 936, imaging RADAR feature maps 940, deep latent ensemblelayer 944, DNN classifier and regressor 948, and output 920. AlthoughDNN feature extractors 916, 924 and 928, deep latent ensemble layer 944,and DNN classifier and regressor 948 are used in this example embodimentof the present disclosure, other complex and sophisticated algorithmscan be used without departing from the spirit and scope of the presentdisclosure. For example, conventional 2D radar imaging algorithms suchas Range-Doppler algorithm can be used. Additional feature extractionalgorithms include, but are not limited to, Histogram of OrientedGradients (HOG), Scale Invariant Feature Transform (SIFT) and Speeded-UpRobust Feature (SURF). Additional classification and regressoralgorithms include, but are not limited to, Decision Tree (DT) andSupport Vector Machine (SVM). A clustering algorithm for objectdetection may include Density-based Clustering of Applications withNoise (DBCAN). The DNN feature extractors 916, 924 and 928, deep latentensemble layer 944 and DNN classifier and regressor 948 are stored forexample, in one or more of the processors/computers such as sensorprocessor(s) 340, computing device(s) 368, vehicle computing device 604,communication devices 608 and 612, CPU 708, and/or servers 614 and 616.Multi-modal calibration and time synchronization block 952 calibratesand synchronizes each of the camera sensor 332, RADAR sensor 324 andLiDAR sensor 320 such that a single frame of each of camera image 904data, imaging RADAR point cloud 908 data and LiDAR point cloud 912 dataare simultaneously captured and provided as inputs to the DNN featureextractors 916, 924, and 928, respectively. Calibration of the camerasensor 332, RADAR sensor 324 and LiDAR sensor 320 include intrinsic andextrinsic calibration of these sensors.

The DNN feature extractors 916, 924, and 928 extract featurerepresentations of objects separately from the camera image 904 data,imaging RADAR point cloud 908 data and LiDAR point cloud 912 data,respectively. The feature extractor is a method that selects and/orcombines variable into features effectively reducing the amount of inputsensor data that has to be processed, while still accurately andcompletely describing the original data set. The output from the DNNfeature extractor 916 produces image feature maps 932, the output fromthe DNN feature extractor 924 produces LiDAR feature maps 936, and theoutput from the DNN feature extractor 928 produces imaging RADAR featuremaps 940. The image feature maps 932, the LiDAR feature maps 936, andthe imaging RADAR feature maps 940 are feature vectors which are eachinput to the deep latent ensemble layer 944 for further processing.According to embodiments of the present disclosure, the feature vectorsof the image feature maps 932, the LiDAR feature maps 936, and theimaging RADAR feature maps 940 have the same dimension. For example, theoutput of the last layer of the feature extractors for each of thefeature extractors 916, 924, and 928 should result in a 3D featuretensor (e.g., matrix) which has the same dimension W×L×(the number ofchannels). The width (W) and the length (L) is the dimension from thetop view of the detected 3D space and the number of channels is theinformation of the position, intensity and learnt global semanticfeatures (for RADAR it also includes range rate information).

The deep latent ensemble layer 944 combines the feature maps whichproduces merged or combined feature vectors. The deep latent ensemblelayer 944 applies average or maximum pooling of the feature tensors(e.g., matrix) from the feature extractors 916, 924 and 928 (since theyall have the same dimension W×L×(number of channels). The output fromthe deep latent ensemble layer 944 is provided to the DNN classifier andregressor 948. The DNN classifier regressor 948 are fully connectednetworks which helps in separating the data of the merged featurevectors into multiple categorical classes and continuous real values(e.g., an object's position and an object's dimension). For example, theDNN classifier regressor 928 outputs 920 the following parameters fordetected objects: object classification (e.g., car, pedestrian, etc.);3D position of the object (e.g., X,Y, Z coordinates of the object); 3Ddimensions of the object (e.g., length, width, height of the object);the direction of the object (e.g., heading); and the velocity of theobject.

FIG. 10 is a block diagram of another example DNN architecture 1000 forfusing LiDAR, imaging RADAR and camera inputs in accordance withembodiments of the present disclosure. The architecture 1000 includesmulti-modal calibration and time synchronization block 1052, cameraimage 1004 data, LiDAR point cloud 1008 data, imaging RADAR point cloud1012 data, raw data fusion layer 1044, DNN feature extractor 1016, fusedfeature maps 1032, DNN classifier and regressor 1048, and output 1020.Although DNN feature extractor 1016 and DNN classifier and regressor1048 are used in this example embodiment of the present disclosure,other complex and sophisticated algorithms can be used without departingfrom the spirit and scope of the present disclosure. The DNN featureextractor 1016 and the DNN classifier and regressor 1048 are stored, forexample, in one or more of the processors/computers such as sensorprocessor(s) 340, computing device(s) 368, vehicle computing device 604,communication devices 608 and 612, CPU 708, and/or servers 614 and 616.Multi-modal calibration and time synchronization block 1052 calibratesand synchronizes each of the camera sensor 332, RADAR sensor 324 andLiDAR sensor 320 such that a single frame of each of camera image 1004data, imaging RADAR point cloud 1008 data and LiDAR point cloud 1012data are simultaneously captured and fused as inputs to raw data fusionlayer 1044. Calibration of the camera sensor 332, RADAR sensor 324, andLiDAR sensor 320 include intrinsic and extrinsic calibration of thesesensors.

Thus, the raw data fusion layer 1044 combines the raw data from each ofthe sensors. This is considered low-level sensor fusion (LLF). Theoutput of the raw fusion layer 1044 is provided to the DNN featureextractor 1016. The DNN feature extractor 1016 extracts featurerepresentations of objects from the fusion (e.g., combination) of thecamera image 1004 data, imaging RADAR point cloud 1008 data, and LiDARpoint cloud 1012 data. The output from the DNN feature extractor 1016produces fused feature maps 1032 which are semantic featurerepresentations of the detected objects. The fused feature maps aresubsequently provided to the DNN classifier and regressor 1048, whichoutputs 1020 the following parameters for detected objects: objectclassification (e.g., car, pedestrian, etc.); 3D position of the object(e.g., X,Y, Z coordinates of the object); 3D dimensions of the object(e.g., length, width, height of the object); the direction of the object(e.g., heading); and the velocity of the object.

The DNN classifier and regressor 1048 is used to estimate the velocityinformation (object velocity from range-rate values, so that the entirearchitecture is end-to-end trainable). With this approach, the totalloss function for the network takes the form of Equation 2 as follows:

L=w _(cls) L _(cls) +w _(loc) L _(loc) +w _(angle) L _(angle) +w_(velocity) L _(velocity)  (2)

Where W_(cls) is the weight for classification loss; L_(cls) is theobject classification loss; W_(loc) is the weight for 3D locationprediction; L_(loc) is the loss for 3D location prediction; W_(angle) isthe weight for heading prediction; L_(angle) is loss for headingprediction; W_(velocity) is the weight for velocity regression; andL_(velocity) is loss of velocity of regression. According to embodimentsof the present disclosure, the DNN is trained by processing many samplesof input training data, and for each training sample, and adjustment ismade to the internal parameters of the DNN using the total lossfunction. The loss function denotes the error between the predictedoutput generated by the network and the ground truth target informationincluded in the training sample. After the parameters in the neuralnetwork are well trained during the prediction stage, the networkoutputs object class prediction, 3D position, 3D dimension, heading, andthe velocity of the object.

FIG. 11 is a flowchart illustrating an example process 1100 for 4Dobject detection by separate camera, imaging RADAR, and LiDAR inaccordance with embodiments of the present disclosure. As illustrated inthis example, process 1100 may include initializing and synchronizingthe camera sensors 332, the LiDAR sensors 320, and the imaging RADARsensors 324 at block 1104. One or more other sensors 304 can be used tocapture geographic position information such as latitude, longitude,elevation, heading, speed, etc., and can comprise, for example, a GPSunit 308 or other sensors 304 as described above.

Once the sensor(s) have been initialized at block 1104, a control systemof the vehicle, such as for example, the vehicle control system 348receives sensor data from camera sensors 332, the LiDAR sensors 320, andthe RADAR sensors 324 in the form of camera image data, LiDAR pointcloud data, and imaging RADAR point cloud data at block 1108.

Using the received sensor data at block 1108, sensor processor 340 orother processors such as for example processors 604, 614 and 618extracts feature representations of objects separately from each of thecamera image data, the LiDAR point cloud data, and the imaging RADARpoint cloud data to generate image feature maps, LiDAR feature maps, andRADAR feature maps at block 1112. Process 1100 continues to block 1116where the image feature maps, LiDAR feature maps, and RADAR feature mapsare combined by a deep latent ensemble layer, for example. Process 1100continues to block 1120 where a DNN classifier and regressor processesthe combined image feature maps, LiDAR feature maps, and RADAR featuremaps and outputs object classification (e.g., car, pedestrian, etc.); 3Dposition of the object (e.g., X,Y,Z coordinates of the object); 3Ddimensions of the object (e.g., length, width, height of the object);the direction of the object (e.g., heading); and the velocity of theobject.

FIG. 12 is a flowchart illustrating an example process 1100 for 4Dobject detection by camera, imaging RADAR, and LiDAR fusion inaccordance with embodiments of the present disclosure. As illustrated inthis example, process 1200 may include initializing and synchronizingthe camera sensors 332, the LiDAR sensors 320, and the RADAR sensors 324at block 1204. One or more other sensors 304 can be used to capturegeographic position information such as latitude, longitude, elevation,heading, speed, etc., and can comprise, for example, a GPS unit 308 orother sensors 304 as described above.

Once the sensor(s) have been initialized at block 1204, a control systemof the vehicle, such as for example, the vehicle control system 348receives sensor data from the camera sensors 332, the LiDAR sensors 320,and the RADAR sensors 324 in the form of camera image data, LiDAR pointcloud data, and imaging RADAR point cloud data at block 1208.

The received sensor data at block 1208 is combined by sensor processor340 or other processors, such as for example, processors 604, 614, and618, to generate fused raw data at block 1212. Sensor processor 340, forexample, extracts feature representations of objects from the fused rawdata to generate fused feature maps at block 1216. Process 1200continues to block 1220 where a DNN classifier and regressor processesthe fused feature maps and outputs object classification (e.g., car,pedestrian, etc.); 3D position of the object (e.g., X,Y,Z coordinates ofthe object); 3D dimensions of the object (e.g., length, width, height ofthe object); the direction of the object (e.g., heading); and thevelocity of the object.

Any of the steps, functions, and operations discussed herein can beperformed continuously and automatically.

The exemplary systems and methods of this disclosure have been describedin relation to vehicle systems and electric vehicles. However, to avoidunnecessarily obscuring the present disclosure, the precedingdescription omits a number of known structures and devices. Thisomission is not to be construed as a limitation of the scope of theclaimed disclosure. Specific details are set forth to provide anunderstanding of the present disclosure. It should, however, beappreciated that the present disclosure may be practiced in a variety ofways beyond the specific detail set forth herein.

Furthermore, while the exemplary embodiments illustrated herein show thevarious components of the system collocated, certain components of thesystem can be located remotely, at distant portions of a distributednetwork, such as a LAN and/or the Internet, or within a dedicatedsystem. Thus, it should be appreciated, that the components of thesystem can be combined into one or more devices, such as a server,communication device, or collocated on a particular node of adistributed network, such as an analog and/or digital telecommunicationsnetwork, a packet-switched network, or a circuit-switched network. Itwill be appreciated from the preceding description, and for reasons ofcomputational efficiency, that the components of the system can bearranged at any location within a distributed network of componentswithout affecting the operation of the system.

Furthermore, it should be appreciated that the various links connectingthe elements can be wired or wireless links, or any combination thereof,or any other known or later developed element(s) that is capable ofsupplying and/or communicating data to and from the connected elements.These wired or wireless links can also be secure links and may becapable of communicating encrypted information. Transmission media usedas links, for example, can be any suitable carrier for electricalsignals, including coaxial cables, copper wire, and fiber optics, andmay take the form of acoustic or light waves, such as those generatedduring radio-wave and infra-red data communications.

While the flowcharts have been discussed and illustrated in relation toa particular sequence of events, it should be appreciated that changes,additions, and omissions to this sequence can occur without materiallyaffecting the operation of the disclosed embodiments, configuration, andaspects.

A number of variations and modifications of the disclosure can be used.It would be possible to provide for some features of the disclosurewithout providing others.

In yet another embodiment, the systems and methods of this disclosurecan be implemented in conjunction with a special purpose computer, aprogrammed microprocessor or microcontroller and peripheral integratedcircuit element(s), an ASIC or other integrated circuit, a digitalsignal processor, a hard-wired electronic or logic circuit such asdiscrete element circuit, a programmable logic device or gate array suchas PLD, PLA, FPGA, PAL, special purpose computer, any comparable means,or the like. In general, any device(s) or means capable of implementingthe methodology illustrated herein can be used to implement the variousaspects of this disclosure. Exemplary hardware that can be used for thepresent disclosure includes computers, handheld devices, telephones(e.g., cellular, Internet enabled, digital, analog, hybrids, andothers), and other hardware known in the art. Some of these devicesinclude processors (e.g., a single or multiple microprocessors), memory,nonvolatile storage, input devices, and output devices. Furthermore,alternative software implementations including, but not limited to,distributed processing or component/target distributed processing,parallel processing, or virtual machine processing can also beconstructed to implement the methods described herein.

In yet another embodiment, the disclosed methods may be readilyimplemented in conjunction with software using object or object-orientedsoftware development environments that provide portable source code thatcan be used on a variety of computer or workstation platforms.Alternatively, the disclosed system may be implemented partially orfully in hardware using standard logic circuits or VLSI design. Whethersoftware or hardware is used to implement the systems in accordance withthis disclosure is dependent on the speed and/or efficiency requirementsof the system, the particular function, and the particular software orhardware systems or microprocessor or microcomputer systems beingutilized.

In yet another embodiment, the disclosed methods may be partiallyimplemented in software that can be stored on a storage medium, executedon programmed general-purpose computer with the cooperation of acontroller and memory, a special purpose computer, a microprocessor, orthe like. In these instances, the systems and methods of this disclosurecan be implemented as a program embedded on a personal computer such asan applet, JAVA® or CGI script, as a resource residing on a server orcomputer workstation, as a routine embedded in a dedicated measurementsystem, system component, or the like. The system can also beimplemented by physically incorporating the system and/or method into asoftware and/or hardware system.

Although the present disclosure describes components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the disclosure is not limited to such standards andprotocols. Other similar standards and protocols not mentioned hereinare in existence and are considered to be included in the presentdisclosure. Moreover, the standards and protocols mentioned herein andother similar standards and protocols not mentioned herein areperiodically superseded by faster or more effective equivalents havingessentially the same functions. Such replacement standards and protocolshaving the same functions are considered equivalents included in thepresent disclosure.

The present disclosure, in various embodiments, configurations, andaspects, includes components, methods, processes, systems and/orapparatus substantially as depicted and described herein, includingvarious embodiments, sub-combinations, and subsets thereof. Those ofskill in the art will understand how to make and use the systems andmethods disclosed herein after understanding the present disclosure. Thepresent disclosure, in various embodiments, configurations, and aspects,includes providing devices and processes in the absence of items notdepicted and/or described herein or in various embodiments,configurations, or aspects hereof, including in the absence of suchitems as may have been used in previous devices or processes, e.g., forimproving performance, achieving ease, and/or reducing cost ofimplementation.

The foregoing discussion of the disclosure has been presented forpurposes of illustration and description. The foregoing is not intendedto limit the disclosure to the form or forms disclosed herein. In theforegoing Detailed Description for example, various features of thedisclosure are grouped together in one or more embodiments,configurations, or aspects for the purpose of streamlining thedisclosure. The features of the embodiments, configurations, or aspectsof the disclosure may be combined in alternate embodiments,configurations, or aspects other than those discussed above. This methodof disclosure is not to be interpreted as reflecting an intention thatthe claimed disclosure requires more features than are expressly recitedin each claim. Rather, as the following claims reflect, inventiveaspects lie in less than all features of a single foregoing disclosedembodiment, configuration, or aspect. Thus, the following claims arehereby incorporated into this Detailed Description, with each claimstanding on its own as a separate preferred embodiment of thedisclosure.

Moreover, though the description of the disclosure has includeddescription of one or more embodiments, configurations, or aspects andcertain variations and modifications, other variations, combinations,and modifications are within the scope of the disclosure, e.g., as maybe within the skill and knowledge of those in the art, afterunderstanding the present disclosure. It is intended to obtain rights,which include alternative embodiments, configurations, or aspects to theextent permitted, including alternate, interchangeable and/or equivalentstructures, functions, ranges, or steps to those claimed, whether or notsuch alternate, interchangeable and/or equivalent structures, functions,ranges, or steps are disclosed herein, and without intending to publiclydedicate any patentable subject matter.

Embodiments include a method for object detection, the method comprisingreceiving, by a processor, sensor data indicative of one or more objectsfor each of a camera subsystem, a LiDAR subsystem, and an imaging RADARsubsystem, wherein the sensor data includes camera image data, LiDARpoint cloud data and imaging RADAR point cloud data and the sensor datais received simultaneously and within one frame for each of the camerasubsystem, the LiDAR subsystem, and the imaging RADAR subsystem,extracting, by the processor, one or more feature representations of theobjects from the camera image data, LiDAR point cloud data and imagingRADAR point cloud data, generating, by the processor, image feature mapsfrom the extracted camera image data, LiDAR feature maps from the LiDARpoint cloud data and imaging RADAR feature maps from the image RADARpoint cloud data, combining, by the processor, the image feature maps,the LiDAR feature maps and the imaging RADAR feature maps to generatemerged feature maps and generating, by the processor, objectclassification, object position, object dimensions, object heading andobject velocity from the merged feature maps.

Aspects of the above method include wherein each of the image featuremaps, LiDAR feature maps and imaging RADAR feature maps are featurevectors that have a same dimension of width, length and a number ofchannels.

Aspects of the above method include wherein the processor includes afeature extractor algorithm to extract the one or more featurerepresentations of the objects from the camera image data, LiDAR pointcloud data and imaging RADAR point cloud data and a classifier andregressor algorithm to generate the object classification, the objectposition, the object dimensions, the object heading and the objectvelocity.

Aspects of the above method include wherein the feature extractoralgorithm includes a Deep Neural Network (DNN) algorithm, a Histogram ofOriented Gradients (HOG) algorithm, a Scale Invariant Feature Transform(SIFT) algorithm or a Speeded-Up Robust Feature (SURF) algorithm.

Aspects of the above method include wherein the classifier and regressoralgorithm includes a Deep Neural Network (DNN) algorithm, a DecisionTree (DT) algorithm or a Support Vector Machine (SVM) algorithm.

Aspects of the above method include further comprising initializing andcalibrating, by the processor, sensors from each of the camerasubsystem, the LiDAR subsystem, and the imaging RADAR subsystem.

Aspects of the above method include wherein calibrating the sensors fromeach of the camera subsystem, the LiDAR subsystem, and the imaging RADARsubsystem includes intrinsic and extrinsic calibrations.

Embodiments include a method for object detection comprising receiving,by a processor, sensor data indicative of one or more objects for eachof a camera subsystem, a LiDAR subsystem, and an imaging RADARsubsystem, wherein the sensor data includes camera image data, LiDARpoint cloud data and imaging RADAR point cloud data and the sensor datais received simultaneously and within one frame for each of the camerasubsystem, the LiDAR subsystem, and the imaging RADAR subsystem,combining, by the processor, camera image data, LiDAR point cloud dataand imaging RADAR point cloud data to create fused raw data, extracting,by the processor, one or more feature representations of the objectsfrom the fused raw data, generating, by the processor, fused featuremaps from the extracted fused raw data and

generating, by the processor, object classification, object position,object dimensions, object heading and object velocity from the fusedfeature maps.

Aspects of the above method include wherein the processor includes afeature extractor algorithm to extract the one or more featurerepresentations of the objects from fused raw data and a classifier andregressor algorithm to generate the object classification, the objectposition, the object dimensions, the object heading and the objectvelocity.

Aspects of the above method include wherein the feature extractoralgorithm includes a Deep Neural Network (DNN) algorithm, a Histogram ofOriented Gradients (HOG) algorithm, a Scale Invariant Feature Transform(SIFT) algorithm or a Speeded-Up Robust Feature (SURF) algorithm.

Aspects of the above method include wherein the classifier and regressoralgorithm includes a Deep Neural Network (DNN) algorithm, a DecisionTree (DT) algorithm or a Support Vector Machine (SVM) algorithm.

Aspects of the above method include further comprising initializing andcalibrating, by the processor, sensors from each of the camerasubsystem, the LiDAR subsystem, and the imaging RADAR subsystem.

Aspects of the above method include wherein calibrating the sensors fromeach of the camera subsystem, the LiDAR subsystem, and the imaging RADARsubsystem includes intrinsic and extrinsic calibrations.

Embodiments include a vehicle control system comprising a processor anda memory coupled with and readable by the processor and storing thereina set of instructions, which when executed by the processor, cause theprocessor to detect objects in a single frame by receiving sensor dataindicative of one or more objects for each of a camera subsystem, aLiDAR subsystem, and an imaging RADAR subsystem, wherein the sensor dataincludes camera image data, LiDAR point cloud data and imaging RADARpoint cloud data and the sensor data is received simultaneously andwithin one frame for each of the camera subsystem, the LiDAR subsystem,and the imaging RADAR subsystem, extracting one or more featurerepresentations of the objects from the camera image data, LiDAR pointcloud data and imaging RADAR point cloud data, generating image featuremaps from the extracted camera image data, LiDAR feature maps from theLiDAR point cloud data and imaging RADAR feature maps from the imageRADAR point cloud data, combining the image feature maps, the LiDARfeature maps and the imaging RADAR feature maps to generate mergedfeature maps and generating object classification, object position,object dimensions, object heading and object velocity from the mergedfeature maps.

Aspects of the above vehicle control system include wherein each of theimage feature maps, LiDAR feature maps and imaging RADAR feature mapsare feature vectors that have a same dimension of width, length and anumber of channels.

Aspects of the above vehicle control system include wherein theprocessor includes a feature extractor algorithm to extract the one ormore feature representations of the objects from the camera image data,LiDAR point cloud data and imaging RADAR point cloud data and aclassifier and regressor algorithm to generate the objectclassification, the object position, the object dimensions, the objectheading and the object velocity.

Aspects of the above vehicle control system include wherein the featureextractor algorithm includes a Deep Neural Network (DNN) algorithm, aHistogram of Oriented Gradients (HOG) algorithm, a Scale InvariantFeature Transform (SIFT) algorithm or a Speeded-Up Robust Feature (SURF)algorithm.

Aspects of the above vehicle control system include wherein theclassifier and regressor algorithm includes a Deep Neural Network (DNN)algorithm, a Decision Tree (DT) algorithm or a Support Vector Machine(SVM) algorithm.

Aspects of the above vehicle control system include further comprisinginitializing and calibrating sensors from each of the camera subsystem,the LiDAR subsystem, and the imaging RADAR subsystem.

Aspects of the above vehicle control system include wherein calibratingthe sensors from each of the camera subsystem, the LiDAR subsystem, andthe imaging RADAR subsystem includes intrinsic and extrinsiccalibrations.

Any one or more of the aspects/embodiments as substantially disclosedherein optionally in combination with any one or more otheraspects/embodiments as substantially disclosed herein.

One or means adapted to perform any one or more of the aboveaspects/embodiments as substantially disclosed herein.

The phrases “at least one,” “one or more,” “or,” and “and/or” areopen-ended expressions that are both conjunctive and disjunctive inoperation. For example, each of the expressions “at least one of A, Band C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “oneor more of A, B, or C,” “A, B, and/or C,” and “A, B, or C” means Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, or A, B and C together.

The term “a” or “an” entity refers to one or more of that entity. Assuch, the terms “a” (or “an”), “one or more,” and “at least one” can beused interchangeably herein. It is also to be noted that the terms“comprising,” “including,” and “having” can be used interchangeably.

The term “automatic” and variations thereof, as used herein, refers toany process or operation, which is typically continuous orsemi-continuous, done without material human input when the process oroperation is performed. However, a process or operation can beautomatic, even though performance of the process or operation usesmaterial or immaterial human input, if the input is received beforeperformance of the process or operation. Human input is deemed to bematerial if such input influences how the process or operation will beperformed. Human input that consents to the performance of the processor operation is not deemed to be “material.”

Aspects of the present disclosure may take the form of an embodimentthat is entirely hardware, an embodiment that is entirely software(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module,” or “system.”Any combination of one or more computer-readable medium(s) may beutilized. The computer-readable medium may be a computer-readable signalmedium or a computer-readable storage medium.

A computer-readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer-readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer-readable storage medium may be any tangible medium that cancontain or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer-readable signal medium may include a propagated data signalwith computer-readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer-readable signal medium may be any computer-readable medium thatis not a computer-readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer-readable medium may be transmitted using anyappropriate medium, including, but not limited to, wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

The terms “determine,” “calculate,” “compute,” and variations thereof,as used herein, are used interchangeably and include any type ofmethodology, process, mathematical operation or technique.

The term “electric vehicle” (EV), also referred to herein as an electricdrive vehicle, may use one or more electric motors or traction motorsfor propulsion. An electric vehicle may be powered through a collectorsystem by electricity from off-vehicle sources, or may be self-containedwith a battery or generator to convert fuel to electricity. An electricvehicle generally includes a rechargeable electricity storage system(RESS) (also called Full Electric Vehicles (FEV)). Power storage methodsmay include: chemical energy stored on the vehicle in on-board batteries(e.g., battery electric vehicle or BEV), on board kinetic energy storage(e.g., flywheels), and/or static energy (e.g., by on-board double-layercapacitors). Batteries, electric double-layer capacitors, and flywheelenergy storage may be forms of rechargeable on-board electrical storage.

The term “hybrid electric vehicle” refers to a vehicle that may combinea conventional (usually fossil fuel-powered) powertrain with some formof electric propulsion. Most hybrid electric vehicles combine aconventional internal combustion engine (ICE) propulsion system with anelectric propulsion system (hybrid vehicle drivetrain). In parallelhybrids, the ICE and the electric motor are both connected to themechanical transmission and can simultaneously transmit power to drivethe wheels, usually through a conventional transmission. In serieshybrids, only the electric motor drives the drivetrain, and a smallerICE works as a generator to power the electric motor or to recharge thebatteries. Power-split hybrids combine series and parallelcharacteristics. A full hybrid, sometimes also called a strong hybrid,is a vehicle that can run on just the engine, just the batteries, or acombination of both. A mid hybrid is a vehicle that cannot be drivensolely on its electric motor, because the electric motor does not haveenough power to propel the vehicle on its own.

The term “rechargeable electric vehicle” or “REV” refers to a vehiclewith on board rechargeable energy storage, including electric vehiclesand hybrid electric vehicles.

What is claimed is:
 1. A method for object detection, the methodcomprising: receiving, by a processor, sensor data indicative of one ormore objects for each of a camera subsystem, a LiDAR subsystem, and animaging RADAR subsystem, wherein the sensor data includes camera imagedata, LiDAR point cloud data and imaging RADAR point cloud data and thesensor data is received simultaneously and within one frame for each ofthe camera subsystem, the LiDAR subsystem, and the imaging RADARsubsystem; extracting, by the processor, one or more featurerepresentations of the objects from the camera image data, LiDAR pointcloud data and imaging RADAR point cloud data; generating, by theprocessor, image feature maps from the extracted camera image data,LiDAR feature maps from the LiDAR point cloud data and imaging RADARfeature maps from the image RADAR point cloud data; combining, by theprocessor, the image feature maps, the LiDAR feature maps and theimaging RADAR feature maps to generate merged feature maps; andgenerating, by the processor, object classification, object position,object dimensions, object heading and object velocity from the mergedfeature maps.
 2. The method of claim 1, wherein each of the imagefeature maps, LiDAR feature maps and imaging RADAR feature maps arefeature vectors that have a same dimension of width, length and a numberof channels.
 3. The method of claim 1, wherein the processor includes afeature extractor algorithm to extract the one or more featurerepresentations of the objects from the camera image data, LiDAR pointcloud data and imaging RADAR point cloud data and a classifier andregressor algorithm to generate the object classification, the objectposition, the object dimensions, the object heading and the objectvelocity.
 4. The method of claim 3, wherein the feature extractoralgorithm includes a Deep Neural Network (DNN) algorithm, a Histogram ofOriented Gradients (HOG) algorithm, a Scale Invariant Feature Transform(SIFT) algorithm or a Speeded-Up Robust Feature (SURF) algorithm.
 5. Themethod of claim 3, wherein the classifier and regressor algorithmincludes a Deep Neural Network (DNN) algorithm, a Decision Tree (DT)algorithm or a Support Vector Machine (SVM) algorithm.
 6. The method ofclaim 1, further comprising initializing and calibrating, by theprocessor, sensors from each of the camera subsystem, the LiDARsubsystem, and the imaging RADAR subsystem.
 7. The method of claim 6,wherein calibrating the sensors from each of the camera subsystem, theLiDAR subsystem, and the imaging RADAR subsystem includes intrinsic andextrinsic calibrations.
 8. A method for object detection, the methodcomprising: receiving, by a processor, sensor data indicative of one ormore objects for each of a camera subsystem, a LiDAR subsystem, and animaging RADAR subsystem, wherein the sensor data includes camera imagedata, LiDAR point cloud data and imaging RADAR point cloud data and thesensor data is received simultaneously and within one frame for each ofthe camera subsystem, the LiDAR subsystem, and the imaging RADARsubsystem; combining, by the processor, the camera image data, LiDARpoint cloud data and imaging RADAR point cloud data to create fused rawdata; extracting, by the processor, one or more feature representationsof the objects from the fused raw data; generating, by the processor,fused feature maps from the extracted fused raw data; and generating, bythe processor, object classification, object position, objectdimensions, object heading and object velocity from the fused featuremaps.
 9. The method of claim 8, wherein the processor includes a featureextractor algorithm to extract the one or more feature representationsof the objects from fused raw data and a classifier and regressoralgorithm to generate the object classification, the object position,the object dimensions, the object heading and the object velocity. 10.The method of claim 9, wherein the feature extractor algorithm includesa Deep Neural Network (DNN) algorithm, a Histogram of Oriented Gradients(HOG) algorithm, a Scale Invariant Feature Transform (SIFT) algorithm ora Speeded-Up Robust Feature (SURF) algorithm.
 11. The method of claim 9,wherein the classifier and regressor algorithm includes a Deep NeuralNetwork (DNN) algorithm, a Decision Tree (DT) algorithm or a SupportVector Machine (SVM) algorithm.
 12. The method of claim 8, furthercomprising initializing and calibrating, by the processor, sensors fromeach of the camera subsystem, the LiDAR subsystem, and the imaging RADARsubsystem.
 13. The method of claim 12, wherein calibrating the sensorsfrom each of the camera subsystem, the LiDAR subsystem, and the imagingRADAR subsystem includes intrinsic and extrinsic calibrations.
 14. Avehicle control system, comprising: a processor; and a memory coupledwith and readable by the processor and storing therein a set ofinstructions, which when executed by the processor, cause the processorto detect objects in a single frame by: receiving sensor data indicativeof one or more objects for each of a camera subsystem, a LiDARsubsystem, and an imaging RADAR subsystem; wherein the sensor dataincludes camera image data, LiDAR point cloud data and imaging RADARpoint cloud data and the sensor data is received simultaneously andwithin one frame for each of the camera subsystem, the LiDAR subsystem,and the imaging RADAR subsystem; extracting one or more featurerepresentations of the objects from the camera image data, LiDAR pointcloud data and imaging RADAR point cloud data; generating image featuremaps from the extracted camera image data, LiDAR feature maps from theLiDAR point cloud data and imaging RADAR feature maps from the imageRADAR point cloud data; combining the image feature maps, the LiDARfeature maps and the imaging RADAR feature maps to generate mergedfeature maps; and generating object classification, object position,object dimensions, object heading and object velocity from the mergedfeature maps.
 15. The vehicle control system of claim 14, wherein eachof the image feature maps, LiDAR feature maps and imaging RADAR featuremaps are feature vectors that have a same dimension of width, length anda number of channels.
 16. The vehicle control system of claim 14,wherein the processor includes a feature extractor algorithm to extractthe one or more feature representations of the objects from the cameraimage data, LiDAR point cloud data and imaging RADAR point cloud dataand a classifier and regressor algorithm to generate the objectclassification, the object position, the object dimensions, the objectheading and the object velocity.
 17. The vehicle control system of claim16, wherein the feature extractor algorithm includes a Deep NeuralNetwork (DNN) algorithm, a Histogram of Oriented Gradients (HOG)algorithm, a Scale Invariant Feature Transform (SIFT) algorithm or aSpeeded-Up Robust Feature (SURF) algorithm.
 18. The vehicle controlsystem of claim 16, wherein the classifier and regressor algorithmincludes a Deep Neural Network (DNN) algorithm, a Decision Tree (DT)algorithm or a Support Vector Machine (SVM) algorithm.
 19. The vehiclecontrol system of claim 14, further comprising initializing andcalibrating sensors from each of the camera subsystem, the LiDARsubsystem, and the imaging RADAR subsystem.
 20. The vehicle controlsystem of claim 19, wherein calibrating the sensors from each of thecamera subsystem, the LiDAR subsystem, and the imaging RADAR subsystemincludes intrinsic and extrinsic calibrations.